{ "skill_name": "hugging-science", "skill_path": "/home/runner/work/skill-scanner-test/skill-scanner-test/scripts/../skills/K-Dense-AI-scientific-agent-skills/skills/hugging-science", "is_safe": false, "max_severity": "HIGH", "findings_count": 5, "findings": [ { "id": "MANIFEST_MISSING_LICENSE_c5ae9be793", "rule_id": "MANIFEST_MISSING_LICENSE", "category": "policy_violation", "severity": "INFO", "title": "Skill does not specify a license", "description": "Skill manifest does not include a 'license' field. Specifying a license helps users understand usage terms.", "file_path": "SKILL.md", "line_number": null, "snippet": null, "remediation": "Add 'license' field to SKILL.md frontmatter (e.g., MIT, Apache-2.0)", "analyzer": "static", "metadata": { "aitech": null, "aitech_name": null, "aisubtech": null, "aisubtech_name": null, "aitech_mappings": [], "aisubtech_mappings": [], "framework_mappings": [], "taxonomy_status": "not_applicable", "taxonomy_source": "rule" } }, { "id": "ACTIVE_REMOTE_ACQUIRE_EXECUTE_2fe4bc290a", "rule_id": "ACTIVE_REMOTE_ACQUIRE_EXECUTE", "category": "command_injection", "severity": "HIGH", "title": "Active instruction acquires and executes remote content", "description": "SKILL.md contains an operative statement that explicitly coordinates remote content acquisition with execution.", "file_path": "SKILL.md", "line_number": 136, "snippet": "Reviewed on 2026-10-01 against the public catalog, current author cards and released SDK source. Catalog fetches and public metadata queries ran live; tiny random ESM2, synthetic dataset and mocked cl", "remediation": "Remove remote execution, or require a locally pinned artifact whose digest or signature is verified before a fixed allowlisted invocation.", "analyzer": "static", "metadata": { "analysis_basis": "bounded_commonmark_statement_grammar", "acquisition_verbs": [ "fetches" ], "execution_verbs": [ "ran" ], "statement_shapes": [ "paragraph" ], "binding_kinds": [ "coordinated_action" ], "semantic_facts": { "evidence_kind": "correlated_behavior", "context_kind": "active_instruction", "evidence_value_class": "untrusted_fetch_execute", "evidence_count": 1, "signal_kind": "compound_flow", "flows": [ { "source_class": "network", "sink_class": "execution", "transforms": [], "cross_file": false, "source_path": "SKILL.md", "sink_path": "SKILL.md" } ], "candidate_flow": { "source_class": "network", "sink_class": "execution", "transforms": [], "cross_file": false, "source_path": "SKILL.md", "sink_path": "SKILL.md" } }, "aitech": "AITech-9.1", "aitech_name": "Model or Agentic System Manipulation", "aisubtech": "AISubtech-9.1.1", "aisubtech_name": "Code Execution", "aitech_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning", "Cisco AI MDL Code: MDL-006", "Cisco AI MDL Code: MDL-007", "Cisco AI MDL Code: MDL-005", "Cisco AI MDL Code: MDL-010", "Cisco AI MDL Code: MDL-012", "Cisco AI MDL Code: MDL-014", "Cisco AI MDL Code: MDL-015", "Cisco AI MDL Code: MDL-019" ], "aisubtech_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning" ], "framework_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning" ], "taxonomy_status": "mapped", "taxonomy_source": "rule" } }, { "id": "MDBLOCK_PYTHON_EVAL_EXEC_ff47aa34f3", "rule_id": "MDBLOCK_PYTHON_EVAL_EXEC", "category": "command_injection", "severity": "HIGH", "title": "Python code block uses eval/exec", "description": "Code block in references/using-models.md at line 42 contains potentially dangerous Python code.", "file_path": "references/using-models.md", "line_number": 42, "snippet": "model = AutoModel.from_pretrained(model_id, revision=revision).eval()", "remediation": "Review the code block for security implications.", "analyzer": "behavioral", "metadata": { "from_code_block": true, "block_language": "python", "aitech": "AITech-9.1", "aitech_name": "Model or Agentic System Manipulation", "aisubtech": "AISubtech-9.1.1", "aisubtech_name": "Code Execution", "aitech_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning", "Cisco AI MDL Code: MDL-006", "Cisco AI MDL Code: MDL-007", "Cisco AI MDL Code: MDL-005", "Cisco AI MDL Code: MDL-010", "Cisco AI MDL Code: MDL-012", "Cisco AI MDL Code: MDL-014", "Cisco AI MDL Code: MDL-015", "Cisco AI MDL Code: MDL-019" ], "aisubtech_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning" ], "framework_mappings": [ "OWASP: LLM04:2026: Supply Chain", "OWASP: ASI04: Agentic Supply Chain Vulnerabilities", "OWASP: ASI05: Unexpected Code Execution (RCE)", "MITRE ATLAS: AML.T0050: Command and Scripting Interpreter", "NIST AML: NISTAML.023: Backdoor Poisoning" ], "taxonomy_status": "mapped", "taxonomy_source": "rule" } }, { "id": "DATA_EXFIL_NETWORK_REQUESTS_0a45cdb9f5", "rule_id": "DATA_EXFIL_NETWORK_REQUESTS", "category": "data_exfiltration", "severity": "MEDIUM", "title": "Outbound network request primitives that can transmit data externally", "description": "Pattern detected: urllib.request.Request(", "file_path": "scripts/fetch_catalog.py", "line_number": 97, "snippet": "req = urllib.request.Request(url, headers={\"User-Agent\": \"hugging-science-skill/1.0\"})", "remediation": "Ensure network operations are necessary and document allowed destinations", "analyzer": "static", "metadata": { "matched_pattern": "urllib\\.request\\.(?:urlopen|Request)\\s*\\(", "matched_text": "urllib.request.Request(", "signature_pattern_index": 3, "signature_match_start": 6, "signature_match_end": 29, "signature_pattern_sha256": "bd8faff6b46ce7415a5dcfbd13f1e9926fe504882c96ed53605bd9f403cf4964", "scanner_category": "SECURITY VIOLATION", "signature_context": "code", "signature_polarity": "active", "source_category": "data_exfiltration", "category_normalization": "native", "semantic_facts": { "evidence_kind": "signature_pattern", "context_kind": "code", "signals": [ { "rule_id": "DATA_EXFIL_NETWORK_REQUESTS", "kind": "signature_polarity", "file_path": "scripts/fetch_catalog.py", "value_class": "active" } ] }, "aitech": "AITech-8.2", "aitech_name": "Data Exfiltration / Exposure", "aisubtech": "AISubtech-8.2.3", "aisubtech_name": "Data Exfiltration via Agent Tooling", "aitech_mappings": [ "OWASP: LLM02:2026: Sensitive Information Disclosure", "OWASP: ASI09: Human-Agent Trust Exploitation", "MITRE ATLAS: AML.T0024.000: Exfiltration via AI Inference API: Infer Training Data Membership", "MITRE ATLAS: AML.T0035: AI Artifact Collection", "MITRE ATLAS: AML.T0037: Data From Local System", "MITRE ATLAS: AML.T0057: LLM Data Leakage", "NIST AML: NISTAML.037: Training Data Attacks" ], "aisubtech_mappings": [ "OWASP: LLM02:2026: Sensitive Information Disclosure", "OWASP: ASI02: Tool Misuse & Exploitation", "OWASP: ASI09: Human-Agent Trust Exploitation", "MITRE ATLAS: AML.T0086: Exfiltration via AI Agent Tool Invocation", "NIST AML: N/A" ], "framework_mappings": [ "OWASP: LLM02:2026: Sensitive Information Disclosure", "OWASP: ASI02: Tool Misuse & Exploitation", "OWASP: ASI09: Human-Agent Trust Exploitation", "MITRE ATLAS: AML.T0086: Exfiltration via AI Agent Tool Invocation", "NIST AML: N/A" ], "taxonomy_status": "mapped", "taxonomy_source": "rule" } }, { "id": "DATA_EXFIL_NETWORK_REQUESTS_d0cf18334c", "rule_id": "DATA_EXFIL_NETWORK_REQUESTS", "category": "data_exfiltration", "severity": "MEDIUM", "title": "Outbound network request primitives that can transmit data externally", "description": "Pattern detected: urllib.request.urlopen(", "file_path": "scripts/fetch_catalog.py", "line_number": 99, "snippet": "with urllib.request.urlopen(req, timeout=30) as resp:", "remediation": "Ensure network operations are necessary and document allowed destinations", "analyzer": "static", "metadata": { "matched_pattern": "urllib\\.request\\.(?:urlopen|Request)\\s*\\(", "matched_text": "urllib.request.urlopen(", "signature_pattern_index": 3, "signature_match_start": 5, "signature_match_end": 28, "signature_pattern_sha256": "bd8faff6b46ce7415a5dcfbd13f1e9926fe504882c96ed53605bd9f403cf4964", "scanner_category": "SECURITY VIOLATION", "signature_context": "code", "signature_polarity": "active", "source_category": "data_exfiltration", "category_normalization": "native", "semantic_facts": { "evidence_kind": "signature_pattern", "context_kind": "code", "signals": [ { "rule_id": "DATA_EXFIL_NETWORK_REQUESTS", "kind": "signature_polarity", "file_path": "scripts/fetch_catalog.py", "value_class": "active" } ] }, "aitech": "AITech-8.2", "aitech_name": "Data Exfiltration / Exposure", "aisubtech": 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N/A" ], "taxonomy_status": "mapped", "taxonomy_source": "rule" } } ], "scan_duration_seconds": 0.26145005226135254, "duration_ms": 261, "analyzers_used": [ "static_analyzer", "bytecode", "pipeline", "correlation", "behavioral_analyzer", "trigger_analyzer" ], "timestamp": "2026-10-21T21:37:51.497731+00:00", "scan_metadata": { "policy_name": "default", "policy_version": "1.0", "policy_preset_base": "balanced", "policy_fingerprint_sha256": "3f48e661bfbc54597f97bbf3dcf2c1cbae2f596ddc79c0b44784efea326b3d1d", "cel": { "mode": "shadow", "runtime": "cel-go", "runtime_version": "v0.32.0;helper=2.3.1", "fact_schema": "v1", "expression_set_hash": "f26459b8f4a46a974d7ab0aaae34127b5b6a40d693e266e12fca03a69a6d26aa", "evaluated": 0, "retained": 5, "would_suppress": 0, "suppressed": 0, "fallbacks": 0, "projection_incomplete": 0, "elapsed_ms": 0.0, "projection_ms": 0.0, "evaluation_ms": 0.0, "errors": [], "per_rule": {}, "suppressed_candidates": [] }, "rule_contract": { "status": "passed", 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