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Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_helpers.py", "start_line": 576, "end_line": null, "start_column": 15 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_helpers.py", "start_line": 583, "end_line": null, "start_column": 36 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_helpers.py", "start_line": 583, "end_line": null, "start_column": 36 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 84, "end_line": null, "start_column": 57 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 84, "end_line": null, "start_column": 57 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 112, "end_line": null, "start_column": 25 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 112, "end_line": null, "start_column": 25 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 179, "end_line": null, "start_column": 25 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 179, "end_line": null, "start_column": 25 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 211, "end_line": null, "start_column": 25 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 211, "end_line": null, "start_column": 25 } ] }, { "id": "PE3", "finding_id": "finding-40e866777c20401ea5aba5e9d1baf42e", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 233, "end_line": null, "start_column": 72 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "\n\nSKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 233, "end_line": null, "start_column": 72 } ] }, { "id": "P6", "finding_id": "finding-31abf796151c4707870c1e734b144f16", "category": "System Prompt Leakage", "pattern": "Direct Prompt Extraction", "severity": "HIGH", "confidence": 0.85, "location": { "file": "scripts/decompose_layers.py", "start_line": 45, "end_line": null, "start_column": 8 }, "finding": "return prompt", "explanation": "Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.", "remediation": "Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.", "code_snippet": " prompt = Path(args.prompt_file).read_text(encoding=\"utf-8\").strip()\n if not prompt:\n raise SystemExit(\"error: prompt file is empty\")\n return prompt\n if args.layers == 4:\n return DEFAULT_PROMPT\n return f\"\"\"Decompose this image into {args.layers} layers with the following specifications:", "intent": null, "tags": [ "System Prompt Leakage" ], "evidence": {}, "match_fingerprint": "18bb822f71209cb3a04d792499f0995d675d1c638e97c90968b496128c99c88d", "occurrences": [ { "file": "scripts/decompose_layers.py", "start_line": 45, "end_line": null, "start_column": 8 } ] }, { "id": "E2", "finding_id": "finding-c11722380c9349dfb3b3e07d0f1fae4c", "category": "Data Exfiltration", "pattern": "Env Variable Harvesting", "severity": "HIGH", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 109, "end_line": 109, "start_column": 16 }, "finding": "list(os.environ)", "explanation": "Code enumerates, copies, or searches environment variables for secrets. Bulk environment access can collect credentials unrelated to the skill's stated purpose.", "remediation": "Read only explicitly required environment variables by name. Avoid enumerating or copying the full environment, and never log or transmit credentials to untrusted destinations.", "code_snippet": " kind: str, source: str, configured_timeout: int | None,\n tmp_path: Path, monkeypatch: pytest.MonkeyPatch,\n) -> None:\n for name in list(os.environ):\n if name.startswith(\"LING_UI_DESIGN_\"):\n monkeypatch.delenv(name)\n dotenv = tmp_path / \".env\"", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "fea96b957ae5ce479376b0c4b20defaead3427461b0cbde8466b2001799c7058", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 109, "end_line": 109, "start_column": 16 } ] }, { "id": "E2", "finding_id": "finding-1ee82d7075a3492ba58e4f339acde479", "category": "Data Exfiltration", "pattern": "Env Variable Harvesting", "severity": "HIGH", "confidence": 0.7, "location": { "file": "tests/test_shared_config.py", "start_line": 214, "end_line": 214, "start_column": 36 }, "finding": "os.environ.items()", "explanation": "Code enumerates, copies, or searches environment variables for secrets. Bulk environment access can collect credentials unrelated to the skill's stated purpose.", "remediation": "Read only explicitly required environment variables by name. Avoid enumerating or copying the full environment, and never log or transmit credentials to untrusted destinations.", "code_snippet": " dotenv = tmp_path / \".env\"\n if source != \"missing\":\n dotenv.write_text(\"LING_UI_DESIGN_API_KEY=\\n\" if source == \"blank\" else \"LING_UI_DESIGN_API_KEY=file-test-key\\n\")\n environment = {k: v for k, v in os.environ.items() if not k.startswith(\"LING_UI_DESIGN_\")}\n if source == \"environment\":\n environment[\"LING_UI_DESIGN_API_KEY\"] = \"environment-test-key\"\n # An exported key must not depend on a readable dotenv file.", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "21b29b57b89a5a3233f697b487f442cbfa19ad73da80ad74d8531f3d6c0dc384", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 214, "end_line": 214, "start_column": 36 } ] }, { "id": "LP3", "finding_id": "finding-4c3887d878644b45b39966dea48278e6", "category": "MCP Least Privilege", "pattern": "Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: env, file_read, file_write, network, shell.", "severity": "MEDIUM", "confidence": 0.7, "location": { "file": "SKILL.md", "start_line": 1, "end_line": null }, "finding": "Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: env, file_read, file_write, network, shell.", "explanation": "Without declared permissions the skill's intent is opaque and cannot be validated.", "remediation": "Declare the skill's tool scope: for Claude Code / Agent Skills SKILL.md, list the tools the skill may invoke in the 'allowed-tools' frontmatter field; for MCP server manifests, add a 'permissions' list naming the required capabilities.", "code_snippet": null, "intent": null, "tags": [ "ASI02" ], "evidence": {}, "match_fingerprint": null, "occurrences": [ { "file": "SKILL.md", "start_line": 1, "end_line": null } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": ".env.example", "start_line": 4, "end_line": null, "start_column": 24 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": ".env.example", "start_line": 4, "end_line": null, "start_column": 24 } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": "README.md", "start_line": 81, "end_line": null, "start_column": 25 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "README.md", "start_line": 81, "end_line": null, "start_column": 25 } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": "references/image-generation.md", "start_line": 10, "end_line": null, "start_column": 14 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "references/image-generation.md", "start_line": 10, "end_line": null, "start_column": 14 } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": "references/layer-decomposition.md", "start_line": 12, "end_line": null, "start_column": 14 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "references/layer-decomposition.md", "start_line": 12, "end_line": null, "start_column": 14 } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": "scripts/_common.py", "start_line": 16, "end_line": null, "start_column": 20 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "scripts/_common.py", "start_line": 16, "end_line": null, "start_column": 20 } ] }, { "id": "E1", "finding_id": "finding-893f437680d546e0ad1a1b2838ca303a", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.6, "location": { "file": "tests/test_shared_config.py", "start_line": 91, "end_line": null, "start_column": 57 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "SKILL_ROOT = Path(__file__).resolve().parents[1]\nENV_FILE = SKILL_ROOT / \".env\"\nENV_EXAMPLE = SKILL_ROOT / \".env.example\"\nDEFAULT_API_BASE = \"https://api.novita.ai/openai/v1/\"\nKEY_SETUP_URL = \"https://novita.ai/\"\n_ENV_NAME = re.compile(r\"^[A-Za-z_][A-Za-z0-9_]*$\")\n", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 91, "end_line": null, "start_column": 57 } ] }, { "id": "AS3", "finding_id": "finding-8210bd7909fc421fbc93be95c335b6d2", "category": "Agent Snooping", "pattern": "Skill Enumeration", "severity": "MEDIUM", "confidence": 0.85, "location": { "file": "scripts/install.py", "start_line": 47, "end_line": null, "start_column": 10 }, "finding": "dir = \".claude/skills", "explanation": "Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.", "remediation": "Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.", "code_snippet": " root = project_root.expanduser().resolve(strict=False)\n else:\n root = (home or Path.home()).expanduser().resolve(strict=False)\n skill_dir = \".claude/skills\" if harness == \"claude\" else \".agents/skills\"\n return root / skill_dir / SKILL_NAME\n\n", "intent": null, "tags": [ "Agent Snooping" ], "evidence": {}, "match_fingerprint": "00ee21ec0dc35f2337f616b8d8bea9d09ba121a7c808a844d9945c3023a297e3", "occurrences": [ { "file": "scripts/install.py", "start_line": 47, "end_line": null, "start_column": 10 } ] }, { "id": "AST4", "finding_id": "finding-d88c7fd1b1ed45609d7121e27d2a578f", "category": "Dangerous Code Execution", "pattern": "subprocess module call", "severity": "MEDIUM", "confidence": 0.7, "location": { "file": "tests/test_shared_config.py", "start_line": 219, "end_line": 222, "start_column": 13, "end_column": 5 }, "finding": "subprocess.run(\n [sys.executable, \"-B\", str(scripts / \"configure.py\"), \"--check\"],\n env=environment, capture_output=True, text=True, check=False,\n )", "explanation": "subprocess module calls execute external commands. Without careful input validation, this enables command injection.", "remediation": "Use subprocess.run() with shell=False and an explicit argument list. Validate all inputs and avoid passing user-controlled data to commands.", "code_snippet": " environment[\"LING_UI_DESIGN_API_KEY\"] = \"environment-test-key\"\n # An exported key must not depend on a readable dotenv file.\n dotenv.write_text(\"invalid dotenv content\")\n result = subprocess.run(\n [sys.executable, \"-B\", str(scripts / \"configure.py\"), \"--check\"],\n env=environment, capture_output=True, text=True, check=False,\n )", "intent": null, "tags": [ "Dangerous Code Execution" ], "evidence": {}, "match_fingerprint": "3cf55c9a59f957f7aaa4d1fe25b3b293eac48ced29a24576e86063cb2da5c449", "occurrences": [ { "file": "tests/test_shared_config.py", "start_line": 219, "end_line": 222, "start_column": 13, "end_column": 5 } ] }, { "id": "SC4", "finding_id": "finding-abf6f12fce584f3fb146f72cf6c151eb", "category": "Supply Chain", "pattern": "Unverifiable Dependency: setuptools has 10 known advisory(ies) (CVE-2013-1633 (Setuptools vulnerable to Man-in-the-middle attacks); CVE-2025-47273 (setuptools has a path traversal vulnerability in PackageIndex.download that lead); CVE-2024-6345 (setuptools vulnerable to Command Injection via package URL) +7 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected", "severity": "LOW", "confidence": 0.4, "location": { "file": "pyproject.toml", "start_line": 2, "end_line": null }, "finding": "setuptools", "explanation": "Dependency has known vulnerabilities (CVEs). Using packages with unpatched security flaws exposes the environment to known exploits.", "remediation": "Update the dependency to a patched version that addresses the known CVE. 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Using packages with unpatched security flaws exposes the environment to known exploits.", "remediation": "Update the dependency to a patched version that addresses the known CVE. Check OSV (osv.dev) or NVD for details on the vulnerability.", "code_snippet": null, "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "09deeb6fdb55899e7b284b11a4a1f240e895ab350d9ef10d889ca4cada77cadd", "occurrences": [ { "file": "pyproject.toml", "start_line": 13, "end_line": null } ] }, { "id": "SC4", "finding_id": "finding-a7f550c0cc924595aadbd3cd7bb36844", "category": "Supply Chain", "pattern": "Unverifiable Dependency: pytest has 2 known advisory(ies) (CVE-2025-71176 (pytest has vulnerable tmpdir handling); CVE-2025-71176 (pytest has vulnerable tmpdir handling)), but the manifest does not pin a version, so it is unknown whether the installed release is affected", "severity": "LOW", "confidence": 0.4, "location": { "file": "pyproject.toml", "start_line": 18, "end_line": null }, "finding": "pytest", "explanation": "Dependency has known vulnerabilities (CVEs). 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original scanner fields otherwise retained." } }, { "skill": { "name": "image-to-editable-ppt", "source": "resources/recommended-skills/image-to-editable-ppt", "scanned_at": "2026-09-30T06:09:29.106612+00:00" }, "risk_assessment": { "score": 100, "severity": "CRITICAL", "recommendation": "DO_NOT_INSTALL", "max_issue_severity": "CRITICAL" }, "components": [ { "path": ".env.example", "type": "other", "lines": 4, "executable": false, "size_bytes": 163, "source_url": null, "source_identity": null, "source_digest": null }, { "path": ".gitignore", "type": "other", "lines": 4, "executable": false, "size_bytes": 38, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "LICENSE", "type": "other", "lines": 21, "executable": false, "size_bytes": 1066, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "README-zh.md", "type": "markdown", "lines": 50, "executable": false, "size_bytes": 2236, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "README.md", "type": "markdown", "lines": 51, "executable": false, "size_bytes": 1946, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "SKILL.md", "type": "markdown", "lines": 209, "executable": false, "size_bytes": 11878, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "agents/openai.yaml", "type": "yaml", "lines": 4, "executable": false, "size_bytes": 227, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "references/iterative-decomposition.md", "type": "markdown", "lines": 101, "executable": false, "size_bytes": 5188, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "references/scene-schema.md", "type": "markdown", "lines": 158, "executable": false, "size_bytes": 5499, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "requirements.txt", "type": "text", "lines": 3, "executable": false, "size_bytes": 40, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/api_services/__init__.py", "type": "python", "lines": 26, "executable": true, "size_bytes": 483, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/api_services/artifact_store.py", "type": "python", "lines": 54, "executable": true, "size_bytes": 2012, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/api_services/image_decomposer.py", "type": "python", "lines": 583, "executable": true, "size_bytes": 24189, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/crop_alpha_assets.py", "type": "python", "lines": 152, "executable": true, "size_bytes": 5549, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/decompose_iterative.py", "type": "python", "lines": 996, "executable": true, "size_bytes": 45375, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/pptx_native.py", "type": "python", "lines": 415, "executable": true, "size_bytes": 22879, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/render_pptx.py", "type": "python", "lines": 330, "executable": true, "size_bytes": 10586, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/scene_dom.py", "type": "python", "lines": 306, "executable": true, "size_bytes": 12948, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/scene_to_pptx.py", "type": "python", "lines": 57, "executable": true, "size_bytes": 2044, "source_url": null, "source_identity": null, "source_digest": null }, { "path": "scripts/validate_pptx.py", "type": "python", "lines": 139, "executable": true, "size_bytes": 5700, "source_url": null, "source_identity": null, "source_digest": null } ], "structured_summaries": [], "issues": [ { "id": "TT3", "finding_id": "finding-f42b8c75ccb94fd4b34d40a9fecacc02", "category": "Data Flow", "pattern": "Tainted flow: 'DOWNLOAD_TIMEOUT_S' from os.environ.get (line 76, credential/environment) → requests.get (network output)", "severity": "CRITICAL", "confidence": 0.9, "location": { "file": "scripts/api_services/image_decomposer.py", "start_line": 253, "end_line": 253, "start_column": 15, "end_column": 63 }, "finding": "requests.get(source, timeout=DOWNLOAD_TIMEOUT_S)", "explanation": "Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.", "remediation": "Never send credentials or environment variables over the network. Use secure credential stores and avoid transmitting secrets in request bodies or URLs.", "code_snippet": " mime = header.split(\";\")[0].split(\":\", 1)[-1] or \"image/png\"\n return base64.b64decode(payload), mime\n if source.startswith((\"http://\", \"https://\")):\n resp = requests.get(source, timeout=DOWNLOAD_TIMEOUT_S)\n if not resp.ok:\n raise RuntimeError(f\"failed to download source HTTP {resp.status_code}: {source[:160]}\")\n mime = (resp.headers.get(\"content-type\") or \"image/png\").split(\";\")[0].strip()", "intent": null, "tags": [ "Data Flow" ], "evidence": {}, "match_fingerprint": "d98fa00a0d304ebb615270d97320ae30e724347abd64847bca7fda3fffea8902", "occurrences": [ { "file": "scripts/api_services/image_decomposer.py", "start_line": 253, "end_line": 253, "start_column": 15, "end_column": 63 } ] }, { "id": "TT3", "finding_id": "finding-d0d4ab015e554a29a30c676fe0b67286", "category": "Data Flow", "pattern": "Tainted flow: 'timeout' from os.environ.get (line 360, credential/environment) → requests.post (network output)", "severity": "CRITICAL", "confidence": 0.9, "location": { "file": "scripts/api_services/image_decomposer.py", "start_line": 298, "end_line": 307, "start_column": 11, "end_column": 5 }, "finding": "requests.post(\n _endpoint(settings[\"LING_BASE_URL\"]),\n headers={\n \"Authorization\": f\"Bearer {settings['LING_API_KEY']}\",\n \"Accept\": \"application/json\",\n },\n ", "explanation": "Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.", "remediation": "Never send credentials or environment variables over the network. Use secure credential stores and avoid transmitting secrets in request bodies or URLs.", "code_snippet": " \"\"\"\n settings = _ling_settings()\n raw, mime = _read_image_bytes(source)\n resp = requests.post(\n _endpoint(settings[\"LING_BASE_URL\"]),\n headers={\n \"Authorization\": f\"Bearer {settings['LING_API_KEY']}\",", "intent": null, "tags": [ "Data Flow" ], "evidence": {}, "match_fingerprint": "af3cf28c06944890b66e19e311c0bfe206960356516ed71cf665fe5a415e5618", "occurrences": [ { "file": "scripts/api_services/image_decomposer.py", "start_line": 298, "end_line": 307, "start_column": 11, "end_column": 5 } ] }, { "id": "TT3", "finding_id": "finding-f435075ab07942b38490151f29686840", "category": "Data Flow", "pattern": "Tainted flow: 'DOWNLOAD_TIMEOUT_S' from os.environ.get (line 76, credential/environment) → requests.get (network output)", "severity": "CRITICAL", "confidence": 0.9, "location": { "file": "scripts/api_services/image_decomposer.py", "start_line": 392, "end_line": 392, "start_column": 19, "end_column": 64 }, "finding": "requests.get(url, timeout=DOWNLOAD_TIMEOUT_S)", "explanation": "Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.", "remediation": "Never send credentials or environment variables over the network. Use secure credential stores and avoid transmitting secrets in request bodies or URLs.", "code_snippet": " if isinstance(item, dict):\n url = item.get(\"url\")\n if url:\n resp = requests.get(url, timeout=DOWNLOAD_TIMEOUT_S)\n if not resp.ok:\n raise RuntimeError(f\"layer download failed HTTP {resp.status_code}: {url[:160]}\")\n return resp.content", "intent": null, "tags": [ "Data Flow" ], "evidence": {}, "match_fingerprint": "3204accf0fef87b74df989438067f3d162986d0b632e07b56ee9751af39cfdd1", "occurrences": [ { "file": "scripts/api_services/image_decomposer.py", "start_line": 392, "end_line": 392, "start_column": 19, "end_column": 64 } ] }, { "id": "PE3", "finding_id": "finding-c0660d08b2414a96a6c3afea525e1dd0", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": ".env.example", "start_line": 1, "end_line": null, "start_column": 20 }, "finding": ".env ", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "# Copy this file to .env and fill in the API key locally.\nLING_BASE_URL=https://api.novita.ai/openai/v1\nLING_API_KEY=sk_xxx\nLING_MODEL=ming-image-0.1-design-layer", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "66ef81c27c03406fd9935e5fc86fc4126d7150c15b8d076fdc528d05cea7c7bf", "occurrences": [ { "file": ".env.example", "start_line": 1, "end_line": null, "start_column": 20 } ] }, { "id": "PE3", "finding_id": "finding-c0660d08b2414a96a6c3afea525e1dd0", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": ".gitignore", "start_line": 4, "end_line": null, "start_column": 0 }, "finding": ".env ", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "# Copy this file to .env and fill in the API key locally.\nLING_BASE_URL=https://api.novita.ai/openai/v1\nLING_API_KEY=sk_xxx\nLING_MODEL=ming-image-0.1-design-layer", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "66ef81c27c03406fd9935e5fc86fc4126d7150c15b8d076fdc528d05cea7c7bf", "occurrences": [ { "file": ".gitignore", "start_line": 4, "end_line": null, "start_column": 0 } ] }, { "id": "PE3", "finding_id": "finding-fbf8dd055ea34e62bf76d27122ff3e0f", "category": "Privilege Escalation", "pattern": "Credential Access", "severity": "HIGH", "confidence": 0.6, "location": { "file": "scripts/api_services/image_decomposer.py", "start_line": 52, "end_line": null, "start_column": 38 }, "finding": ".env\"", "explanation": "Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.", "remediation": "Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.", "code_snippet": "# ==================== Config ====================\n\n_SKILL_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"..\"))\n_ENV_FILE = os.path.join(_SKILL_DIR, \".env\")\n_ENV_EXAMPLE = os.path.join(_SKILL_DIR, \".env.example\")\n_LING_KEYS = (\"LING_BASE_URL\", \"LING_MODEL\", \"LING_API_KEY\")\n", "intent": null, "tags": [ "Privilege Escalation" ], "evidence": {}, "match_fingerprint": "933e7e6dda63d615cb9ac0846682521855cd79c351e84f3fa87d934c18b752db", "occurrences": [ { "file": "scripts/api_services/image_decomposer.py", "start_line": 52, "end_line": null, "start_column": 38 } ] }, { "id": "P6", "finding_id": "finding-2b40ed6370bd4c04b0b8797f7cdb5d8f", "category": "System Prompt Leakage", "pattern": "Direct Prompt Extraction", "severity": "HIGH", "confidence": 0.85, "location": { "file": "scripts/decompose_iterative.py", "start_line": 555, "end_line": null, "start_column": 12 }, "finding": "return prompt", "explanation": "Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.", "remediation": "Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.", "code_snippet": " plan = load_plan(args.plan or img_dir)\n if plan:\n try:\n return prompt_from_plan(plan), (\"--plan\" if args.plan else \"decompose_plan.json\")\n except ValueError as e:\n raise SystemExit(f\"could not read a prompt from the layer plan: {e}\\n\"\n f\" → pass --prompt \\\"N layers: 1 ..., 2 ..., N background\\\"\")", "intent": null, "tags": [ "System Prompt Leakage" ], "evidence": {}, "match_fingerprint": "18bb822f71209cb3a04d792499f0995d675d1c638e97c90968b496128c99c88d", "occurrences": [ { "file": "scripts/decompose_iterative.py", "start_line": 555, "end_line": null, "start_column": 12 } ] }, { "id": "E1", "finding_id": "finding-53647b7d833c49ebbfa67e0f81a9a2c2", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.5, "location": { "file": ".env.example", "start_line": 2, "end_line": null, "start_column": 14 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "# Copy this file to .env and fill in the API key locally.\nLING_BASE_URL=https://api.novita.ai/openai/v1\nLING_API_KEY=sk_xxx\nLING_MODEL=ming-image-0.1-design-layer", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": ".env.example", "start_line": 2, "end_line": null, "start_column": 14 } ] }, { "id": "E1", "finding_id": "finding-53647b7d833c49ebbfa67e0f81a9a2c2", "category": "Data Exfiltration", "pattern": "External Transmission", "severity": "MEDIUM", "confidence": 0.5, "location": { "file": "README.md", "start_line": 32, "end_line": null, "start_column": 14 }, "finding": "https://api.novita.ai/", "explanation": "Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.", "remediation": "Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.", "code_snippet": "# Copy this file to .env and fill in the API key locally.\nLING_BASE_URL=https://api.novita.ai/openai/v1\nLING_API_KEY=sk_xxx\nLING_MODEL=ming-image-0.1-design-layer", "intent": null, "tags": [ "Data Exfiltration" ], "evidence": {}, "match_fingerprint": "a9276aecbd94fb1c6fdcb5c8c39ef92d55162798549b7c29e84d8e1b6d26112a", "occurrences": [ { "file": "README.md", "start_line": 32, "end_line": null, "start_column": 14 } ] }, { "id": "LP3", "finding_id": "finding-29e05de356764efda9ac4a844d8175b8", "category": "MCP Least Privilege", "pattern": "Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: env, file_read, file_write, network, shell.", "severity": "MEDIUM", "confidence": 0.7, "location": { "file": "SKILL.md", "start_line": 1, "end_line": null }, "finding": "Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: env, file_read, file_write, network, shell.", "explanation": "Without declared permissions the skill's intent is opaque and cannot be validated.", "remediation": "Declare the skill's tool scope: for Claude Code / Agent Skills SKILL.md, list the tools the skill may invoke in the 'allowed-tools' frontmatter field; for MCP server manifests, add a 'permissions' list naming the required capabilities.", "code_snippet": null, "intent": null, "tags": [ "ASI02" ], "evidence": {}, "match_fingerprint": null, "occurrences": [ { "file": "SKILL.md", "start_line": 1, "end_line": null } ] }, { "id": "TT2", "finding_id": "finding-25423303140440bf85671234d64855a8", "category": "Data Flow", "pattern": "Tainted flow: 'mime' from requests.get (line 256, network input) → requests.post (network output)", "severity": "MEDIUM", "confidence": 0.65, "location": { "file": "scripts/api_services/image_decomposer.py", "start_line": 298, "end_line": 307, "start_column": 11, "end_column": 5 }, "finding": "requests.post(\n _endpoint(settings[\"LING_BASE_URL\"]),\n headers={\n \"Authorization\": f\"Bearer {settings['LING_API_KEY']}\",\n \"Accept\": \"application/json\",\n },\n ", "explanation": "Data from a source is assigned to a variable that is later passed to a sink, creating a variable-mediated taint flow.", "remediation": "Validate tainted variables before passing them to sinks. Use allowlists, type checks, or sanitization functions on data from external sources.", "code_snippet": " \"\"\"\n settings = _ling_settings()\n raw, mime = _read_image_bytes(source)\n resp = requests.post(\n _endpoint(settings[\"LING_BASE_URL\"]),\n headers={\n \"Authorization\": f\"Bearer {settings['LING_API_KEY']}\",", "intent": null, "tags": [ "Data Flow" ], "evidence": {}, "match_fingerprint": "1f2d1266d8cb86be2b5bcbe065d573d8475d2f5ef939a892b6f246445daf8176", "occurrences": [ { "file": "scripts/api_services/image_decomposer.py", "start_line": 298, "end_line": 307, "start_column": 11, "end_column": 5 } ] }, { "id": "AST4", "finding_id": "finding-3e2a48360c4240b3ae53df04d05521da", "category": "Dangerous Code Execution", "pattern": "subprocess module call", "severity": "MEDIUM", "confidence": 0.7, "location": { "file": "scripts/render_pptx.py", "start_line": 91, "end_line": 96, "start_column": 15, "end_column": 9 }, "finding": "subprocess.Popen(\n command,\n stdout=log,\n stderr=subprocess.STDOUT,\n start_new_session=(os.name == \"posix\"),\n )", "explanation": "subprocess module calls execute external commands. Without careful input validation, this enables command injection.", "remediation": "Use subprocess.run() with shell=False and an explicit argument list. Validate all inputs and avoid passing user-controlled data to commands.", "code_snippet": " exit_seen: float | None = None\n\n with log_path.open(\"w\", encoding=\"utf-8\") as log:\n proc = subprocess.Popen(\n command,\n stdout=log,\n stderr=subprocess.STDOUT,", "intent": null, "tags": [ "Dangerous Code Execution" ], "evidence": {}, "match_fingerprint": "214a4723e6a6fe13d1c8fd457f3752b3159a4629ff84c4d7829a720e7e82b788", "occurrences": [ { "file": "scripts/render_pptx.py", "start_line": 91, "end_line": 96, "start_column": 15, "end_column": 9 } ] }, { "id": "SC1", "finding_id": "finding-d7ad7e101b2f409fbb02c2e408182a48", "category": "Supply Chain", "pattern": "Unpinned Dependencies", "severity": "LOW", "confidence": 0.5, "location": { "file": "requirements.txt", "start_line": 1, "end_line": null, "start_column": 0 }, "finding": "Pillow>=10.0", "explanation": "Dependencies lack version pinning, allowing potential malicious package updates. Consider pinning versions.", "remediation": "Pin all dependency versions in requirements.txt or pyproject.toml. Use exact versions (==) or compatible ranges. Run pip-audit regularly.", "code_snippet": "Pillow>=10.0\nnumpy>=1.26\nrequests>=2.31", "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "9fed5cf3427d0ac503a56553b1a3f08f715d4e92c65fe2c1b55c1ac0fc45a42c", "occurrences": [ { "file": "requirements.txt", "start_line": 1, "end_line": null, "start_column": 0 } ] }, { "id": "SC4", "finding_id": "finding-7a5edf5e0e094b778b7b72e06a9f56b3", "category": "Supply Chain", "pattern": "Unverifiable Dependency: Pillow has 16 known advisory(ies) (CVE-2016-2533 (Pillow buffer overflow in ImagingPcdDecode); CVE-2023-50447 (Arbitrary Code Execution in Pillow); CVE-2021-27922 (Pillow Uncontrolled Resource Consumption) +13 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected", "severity": "LOW", "confidence": 0.4, "location": { "file": "requirements.txt", "start_line": 1, "end_line": null }, "finding": "Pillow", "explanation": "Dependency has known vulnerabilities (CVEs). Using packages with unpatched security flaws exposes the environment to known exploits.", "remediation": "Update the dependency to a patched version that addresses the known CVE. Check OSV (osv.dev) or NVD for details on the vulnerability.", "code_snippet": null, "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "dd8e79d61dd101079536d6680ca830a64bdd1dfc09e23b77bb2413ead7384d2c", "occurrences": [ { "file": "requirements.txt", "start_line": 1, "end_line": null } ] }, { "id": "SC1", "finding_id": "finding-24231099918d4fd4ab33dc5558bddb04", "category": "Supply Chain", "pattern": "Unpinned Dependencies", "severity": "LOW", "confidence": 0.5, "location": { "file": "requirements.txt", "start_line": 2, "end_line": null, "start_column": 0 }, "finding": "numpy>=1.26", "explanation": "Dependencies lack version pinning, allowing potential malicious package updates. Consider pinning versions.", "remediation": "Pin all dependency versions in requirements.txt or pyproject.toml. Use exact versions (==) or compatible ranges. Run pip-audit regularly.", "code_snippet": "Pillow>=10.0\nnumpy>=1.26\nrequests>=2.31", "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "f8431feb58b20e51e6428388864f96a52a7117e8918a4380119503d38c8cc3ca", "occurrences": [ { "file": "requirements.txt", "start_line": 2, "end_line": null, "start_column": 0 } ] }, { "id": "SC4", "finding_id": "finding-fa018354bfef4ee9884a1517bbc0706d", "category": "Supply Chain", "pattern": "Unverifiable Dependency: numpy has 16 known advisory(ies) (CVE-2014-1859 (Numpy arbitrary file write via symlink attack); CVE-2021-41495 (NumPy NULL Pointer Dereference); CVE-2021-33430 (NumPy Buffer Overflow (Disputed)) +13 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected", "severity": "LOW", "confidence": 0.4, "location": { "file": "requirements.txt", "start_line": 2, "end_line": null }, "finding": "numpy", "explanation": "Dependency has known vulnerabilities (CVEs). Using packages with unpatched security flaws exposes the environment to known exploits.", "remediation": "Update the dependency to a patched version that addresses the known CVE. Check OSV (osv.dev) or NVD for details on the vulnerability.", "code_snippet": null, "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "4f79ecef9fb365f08743ec4501c2301acbdffbc92cf5eb8581b3feba433c9899", "occurrences": [ { "file": "requirements.txt", "start_line": 2, "end_line": null } ] }, { "id": "SC1", "finding_id": "finding-f4b4f9149a384bad9af272bb80f29385", "category": "Supply Chain", "pattern": "Unpinned Dependencies", "severity": "LOW", "confidence": 0.5, "location": { "file": "requirements.txt", "start_line": 3, "end_line": null, "start_column": 0 }, "finding": "requests>=2.31\n", "explanation": "Dependencies lack version pinning, allowing potential malicious package updates. Consider pinning versions.", "remediation": "Pin all dependency versions in requirements.txt or pyproject.toml. Use exact versions (==) or compatible ranges. Run pip-audit regularly.", "code_snippet": "Pillow>=10.0\nnumpy>=1.26\nrequests>=2.31", "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "efdebf639e751519b1b47549af044c3d58d1b4e9ea5547082faaab9130d9c472", "occurrences": [ { "file": "requirements.txt", "start_line": 3, "end_line": null, "start_column": 0 } ] }, { "id": "SC4", "finding_id": "finding-4f70dad5557348c3a12600853418729c", "category": "Supply Chain", "pattern": "Unverifiable Dependency: requests has 16 known advisory(ies) (CVE-2014-1830 (Exposure of Sensitive Information to an Unauthorized Actor in Requests); CVE-2024-47081 (Requests vulnerable to .netrc credentials leak via malicious URLs); CVE-2024-35195 (Requests `Session` object does not verify requests after making first request wi) +13 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected", "severity": "LOW", "confidence": 0.4, "location": { "file": "requirements.txt", "start_line": 3, "end_line": null }, "finding": "requests", "explanation": "Dependency has known vulnerabilities (CVEs). Using packages with unpatched security flaws exposes the environment to known exploits.", "remediation": "Update the dependency to a patched version that addresses the known CVE. Check OSV (osv.dev) or NVD for details on the vulnerability.", "code_snippet": null, "intent": null, "tags": [ "Supply Chain" ], "evidence": {}, "match_fingerprint": "09deeb6fdb55899e7b284b11a4a1f240e895ab350d9ef10d889ca4cada77cadd", "occurrences": [ { "file": "requirements.txt", "start_line": 3, "end_line": null } ] } ], "suppressed_count": 0, "suppressed": [], "metadata": { "has_executable_scripts": true, "skillspector_version": "2.12.0", "llm_requested": false, "llm_available": false, "meta_analysis_applied": false, "inference_usage": [], "llm_provenance": { "schema_version": 1, "provider": { "configured_adapter": "unknown", "resolved_adapter": "unknown", "effective_adapter": "not_applicable", "effective_adapters": [], "service": "unknown", "routing": { "deployment_override": null, "deployment_source": "unknown", "api_version": null, "api_version_source": "unknown" } }, "analyzers": [ { "analyzer_id": "mcp_tool_poisoning", "model": "z-ai/glm-5.2", "model_source": "resolved_configuration", "analyzer_revision": { "value": "2.12.0", "source": "skillspector_package", "source_revision": { "value": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "source": "package_vcs_metadata" } } }, { "analyzer_id": "semantic_developer_intent", "model": "z-ai/glm-5.2", "model_source": "resolved_configuration", "analyzer_revision": { "value": "2.12.0", "source": "skillspector_package", "source_revision": { "value": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "source": "package_vcs_metadata" } } }, { "analyzer_id": "semantic_quality_policy", "model": "z-ai/glm-5.2", "model_source": "resolved_configuration", "analyzer_revision": { "value": "2.12.0", "source": "skillspector_package", "source_revision": { "value": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "source": "package_vcs_metadata" } } }, { "analyzer_id": "semantic_security_discovery", "model": "z-ai/glm-5.2", "model_source": "resolved_configuration", "analyzer_revision": { "value": "2.12.0", "source": "skillspector_package", "source_revision": { "value": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "source": "package_vcs_metadata" } } }, { "analyzer_id": "meta_analyzer", "model": "z-ai/glm-5.2", "model_source": "resolved_configuration", "analyzer_revision": { "value": "2.12.0", "source": "skillspector_package", "source_revision": { "value": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "source": "package_vcs_metadata" } } } ], "sampling": { "temperature": { "requested": null, "source": "provider_default", "forwarded_to_client": null, "adapter_support": false, "provider_support": "unknown" }, "seed": { "requested": null, "source": "unset", "forwarded_to_client": null, "adapter_support": false, "provider_support": "unknown" }, "reasoning_effort": { "requested": null, "source": "provider_default", "forwarded_to_client": null, "adapter_support": false, "provider_support": "unknown" } }, "determinism": { "classification": "not_applicable", "control_status": "not_applied", "provider_guarantee": false, "reason": "LLM analysis was not executed for this scan." } }, "filtering_mode": "heuristic" }, "execution_successful": true, "analysis_completeness": { "total_components": 20, "scanned_components": 18, "coverage_percent": 90.0, "is_complete": false, "status": "partial", "execution_successful": true, "fully_inspected_files": 18, "partially_inspected_files": 2, "entirely_uninspected_files": 0, "ledger_exceptions": [ { "outcome": "partial", "phase": "static", "reason_code": "static_parse_limit", "message": "A security-relevant expression exceeded a bounded static parser's span limit.", "path": "SKILL.md", "start_line": null, "end_line": null, "fatal": false, "analyzers": [ "static_patterns_tool_misuse" ] }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 10, "end_line": 10, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 65, "end_line": 65, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 72, "end_line": 72, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 76, "end_line": 76, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 81, "end_line": 81, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 92, "end_line": 92, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 134, "end_line": 134, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 149, "end_line": 149, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 151, "end_line": 151, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 153, "end_line": 153, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 157, "end_line": 157, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 167, "end_line": 167, "fatal": false }, { "outcome": "partial", "phase": "reference_resolution", "reason_code": "reference_missing", "message": "A local path-like reference does not match any bundled artifact, such as a file the skill writes at runtime.", "path": "SKILL.md", "start_line": 170, "end_line": 170, "fatal": false }, { "outcome": "partial", "phase": "static", "reason_code": "output_limit", "message": "Inspection reached its configured output limit.", "path": "requirements.txt", "start_line": null, "end_line": null, "fatal": false, "analyzers": [ "static_patterns_supply_chain_dependencies_output_limit" ] } ], "scope_exclusions": [], "analyzer_statuses": [ { "analyzer_id": "artifact_integrity", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "behavioral_ast", "status": "completed", "planned_work": 10, "completed": 10, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "behavioral_taint_tracking", "status": "completed", "planned_work": 10, "completed": 10, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "bundled_execution_surface", "status": "not_applicable", "planned_work": 0, "completed": 0, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0, "reason_code": "no_applicable_files", "message": "No files matched this analyzer's applicability contract." }, { "analyzer_id": "mcp_least_privilege", "status": "completed", "planned_work": 11, "completed": 11, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "mcp_rug_pull", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "mcp_tool_poisoning", "status": "completed", "planned_work": 1, "completed": 1, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "meta_analyzer", "status": "disabled", "planned_work": 0, "completed": 0, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0, "reason_code": "disabled_by_configuration", "message": "Analyzer was disabled by the requested configuration." }, { "analyzer_id": "semantic_developer_intent", "status": "disabled", "planned_work": 0, "completed": 0, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0, "reason_code": "disabled_by_configuration", "message": "Analyzer was disabled by the requested configuration." }, { "analyzer_id": "semantic_quality_policy", "status": "disabled", "planned_work": 0, "completed": 0, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0, "reason_code": "disabled_by_configuration", "message": "Analyzer was disabled by the requested configuration." }, { "analyzer_id": "semantic_security_discovery", "status": "disabled", "planned_work": 0, "completed": 0, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0, "reason_code": "disabled_by_configuration", "message": "Analyzer was disabled by the requested configuration." }, { "analyzer_id": "static_patterns_agent_snooping", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_anti_refusal", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_data_exfiltration", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_deserialization", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_excessive_agency", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_harmful_content", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_memory_poisoning", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_output_handling", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_privilege_escalation", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_prompt_injection", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_rogue_agent", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_ssrf", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_supply_chain", "status": "degraded", "planned_work": 21, "completed": 20, "partial": 1, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_system_prompt_leakage", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_patterns_tool_misuse", "status": "degraded", "planned_work": 20, "completed": 19, "partial": 1, "skipped": 0, "failed": 0, "unaccounted": 0 }, { "analyzer_id": "static_yara", "status": "completed", "planned_work": 20, "completed": 20, "partial": 0, "skipped": 0, "failed": 0, "unaccounted": 0 } ], "references": [ { "source_path": "SKILL.md", "line": 10, "column": 50, "evidence": "This skill is self-contained. Run every `python3 scripts/...` command from this skill directory, so `scripts/` resolves. Paths such as `/absolute/path/to/case` ", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "inline_command" }, { "source_path": "SKILL.md", "line": 14, "column": 215, "evidence": "ot print that file or pass the key on the command line. If it is missing, copy `.env.example` to `.env` and fill it in.", "target_path": ".env.example", "status": "resolved", "disposition": "analyzed", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 58, "column": 9, "evidence": "python3 scripts/decompose_iterative.py /absolute/path/to/case \\", "target_path": "scripts/decompose_iterative.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 65, "column": 24, "evidence": "While it runs, create `scene.json` from the source image dimensions and build everything that does not depend on returned pixels:", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 72, "column": 47, "evidence": "If useful, compile this native-only scene to `work/scaffold.pptx` and inspect text wrapping and container geometry:", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 75, "column": 9, "evidence": "python3 scripts/scene_to_pptx.py /absolute/path/to/case \\", "target_path": "scripts/scene_to_pptx.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 76, "column": 28, "evidence": "--scene scene.json --out work/scaffold.pptx", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 81, "column": 135, "evidence": " the root result arrives, if its canvas differs from the source canvas, change `scene.canvas` to the returned canvas and scale all pixel-based native geometry o", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 92, "column": 10, "evidence": "Inspect `case/layers/auto_preview.png`. Mechanical signals in `auto_report.json` are clues; visual inspection decides whether a layer is usable.", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 92, "column": 64, "evidence": "Inspect `case/layers/auto_preview.png`. Mechanical signals in `auto_report.json` are clues; visual inspection decides whether a layer is usable.", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 109, "column": 9, "evidence": "python3 scripts/decompose_iterative.py /absolute/path/to/case \\", "target_path": "scripts/decompose_iterative.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 117, "column": 9, "evidence": "python3 scripts/decompose_iterative.py /absolute/path/to/case \\", "target_path": "scripts/decompose_iterative.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 122, "column": 46, "evidence": "Read [references/iterative-decomposition.md](references/iterative-decomposition.md) only when deciding a refinement prompt, handling a stuck layer, or interpret", "target_path": "references/iterative-decomposition.md", "status": "resolved", "disposition": "analyzed", "reference_kind": "markdown_link" }, { "source_path": "SKILL.md", "line": 122, "column": 7, "evidence": "Read [references/iterative-decomposition.md](references/iterative-decomposition.md) only when deciding a refinement prompt, handling a stuck layer, or interpret", "target_path": "references/iterative-decomposition.md", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 131, "column": 9, "evidence": "python3 scripts/decompose_iterative.py /absolute/path/to/case --assemble", "target_path": "scripts/decompose_iterative.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 134, "column": 149, "evidence": "ned children are resized and mapped back to their exact root-canvas positions; `manifest.json` records their origins and root boxes.", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 139, "column": 9, "evidence": "python3 scripts/crop_alpha_assets.py \\", "target_path": "scripts/crop_alpha_assets.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 149, "column": 18, "evidence": "### 5. Complete `scene.json` and compile", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 151, "column": 35, "evidence": "Read [references/scene-schema.md](references/scene-schema.md) when authoring `scene.json`. Use `layers_final/manifest.json.canvas` as the final `scene.canvas`. ", "target_path": "references/scene-schema.md", "status": "resolved", "disposition": "analyzed", "reference_kind": "markdown_link" }, { "source_path": "SKILL.md", "line": 151, "column": 7, "evidence": "Read [references/scene-schema.md](references/scene-schema.md) when authoring `scene.json`. Use `layers_final/manifest.json.canvas` as the final `scene.canvas`. ", "target_path": "references/scene-schema.md", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 151, "column": 79, "evidence": "Read [references/scene-schema.md](references/scene-schema.md) when authoring `scene.json`. Use `layers_final/manifest.json.canvas` as the final `scene.canvas`. ", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 151, "column": 97, "evidence": "/scene-schema.md](references/scene-schema.md) when authoring `scene.json`. Use `layers_final/manifest.json.canvas` as the final `scene.canvas`. Coordinates rema", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 151, "column": 146, "evidence": "n authoring `scene.json`. Use `layers_final/manifest.json.canvas` as the final `scene.canvas`. Coordinates remain in pixels until compilation.", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 153, "column": 310, "evidence": ". Do not patch the binary scaffold PPTX or recreate its native objects; update `scene.json` and compile a fresh final PPTX.", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 157, "column": 28, "evidence": "- the clean last layer as `background.asset`;", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 166, "column": 9, "evidence": "python3 scripts/scene_to_pptx.py /absolute/path/to/case \\", "target_path": "scripts/scene_to_pptx.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 167, "column": 28, "evidence": "--scene scene.json --out final/output.pptx", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 170, "column": 50, "evidence": "Text or layout corrections require only editing `scene.json` and rerunning this command. Do not refetch layers for a local text, shape, position, color, or z-or", "target_path": null, "status": "missing", "disposition": "partial", "reference_kind": "quoted_or_code" }, { "source_path": "SKILL.md", "line": 179, "column": 9, "evidence": "python3 scripts/validate_pptx.py /absolute/path/to/case/final/output.pptx \\", "target_path": "scripts/validate_pptx.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" }, { "source_path": "SKILL.md", "line": 182, "column": 9, "evidence": "python3 scripts/render_pptx.py /absolute/path/to/case/final/output.pptx \\", "target_path": "scripts/render_pptx.py", "status": "resolved", "disposition": "analyzed", "reference_kind": "plain_path" } ], "limitations": [ "Analyzer static_patterns_supply_chain status: degraded.", "Analyzer static_patterns_tool_misuse status: degraded." ], "findings_before_filtering": 18, "findings_after_filtering": 18 }, "review_provenance": { "review_date": "2026-09-30", "repository": "https://github.com/inclusionAI/ling-cookbook", "source_commit": "c4a519f51bc238acefef009f957540d91ddd41fd", "source_commit_date": "2026-09-23T06:53:28Z", "repository_directory": "resources/recommended-skills/image-to-editable-ppt", "frontmatter_name": "image-to-editable-ppt", "scanner_origin": "https://github.com/NVIDIA/SkillSpector.git", "scanner_upstream_revision": "2226747e4ca97198bb82faf5085b8a75f2e1dc02", "raw_report_sha256": "42cf5d6fa58cc7ec417c1ee7f4a8eac7a99cb9b07e4fb410f5ca997c73173f2b", "artifact_file_count": 20, "artifact_total_bytes": 160046, "artifact_manifest": "artifact-manifest.json", "normalization": "Machine absolute skill.source replaced with repository-relative source; original scanner fields otherwise retained." } } ] }