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mukul975
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Demand-driven expansion targeting the fastest-growing 2025-2026 threat and
skills categories (ISC2/WEF/CrowdStrike/Mandiant signals):
- AI Security (NEW domain, 12 skills): LLM red-teaming with garak/PyRIT,
prompt injection (direct/indirect/RAG), MCP tool-poisoning, agentic tool
invocation, guardrails, model/data poisoning, system-prompt leakage,
embedding/vector weaknesses, model extraction, continuous red-teaming
- Supply Chain Security (NEW domain, 5 skills): SBOMs, dependency confusion,
malicious-npm triage, typosquatting, SLSA/Sigstore provenance
- Hardware & Firmware Security (NEW domain, 4 skills): CHIPSEC/UEFI audit,
Secure Boot bypass, TPM measured-boot attestation, ESP bootkit hunting
- Identity (10): Entra ID/ROADtools, GraphRunner, AADInternals, ADCS/Certipy,
shadow credentials, coercion, BloodHound CE, device-code phishing, SSO abuse
- Cloud-native (8): Stratus, Pacu, CloudFox, container escape, K8s RBAC,
Falco, Trivy, kube-bench
- Offensive C2 (6): Sliver, Havoc, NetExec, DPAPI, NTLM relay ESC8, redirectors
- DFIR (6): Hayabusa, Chainsaw, KAPE, Velociraptor, EZ Tools, Plaso
- Backfill (4): OpenCTI, MISP, honeytokens, post-quantum crypto migration
Each skill follows the repo taxonomy (SKILL.md + references/{standards,api-reference}.md
+ scripts/agent.py + LICENSE), with researched real tool commands (no placeholders),
complete frontmatter, and ATT&CK/ATLAS + NIST CSF mappings. Updates README domain
table, skill count, and index.json.
33 lines
1.5 KiB
Markdown
33 lines
1.5 KiB
Markdown
# Standards and References — Auditing MCP Servers for Tool Poisoning
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## MITRE ATLAS References
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| Technique ID | Name | Tactic | Rationale |
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|--------------|------|--------|-----------|
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| AML.T0010 | ML Supply Chain Compromise | Initial Access | A poisoned third-party MCP server compromises the agent supply chain |
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| AML.T0051.001 | LLM Prompt Injection: Indirect | Initial Access | Poisoned tool descriptions are indirect injection into agent context |
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| AML.T0053 | LLM Plugin Compromise | Execution | MCP tools are the agent's plugins; poisoning compromises them |
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| AML.T0057 | LLM Data Leakage | Exfiltration | Poisoned tools commonly exfiltrate files/secrets |
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## NIST AI RMF References
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| ID | Name | Rationale |
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|----|------|-----------|
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| MANAGE-2.2 | Mechanisms are in place and applied to sustain the value of deployed AI systems | Auditing third-party MCP tools manages/sustains safe agent operation |
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## OWASP MCP Top 10 (2025)
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| ID | Name | Rationale |
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|----|------|-----------|
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| MCP03:2025 | Tool Poisoning | Primary risk this skill audits |
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| MCP01:2025 | Prompt Injection | Poisoned descriptions inject the agent |
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## Official Resources
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- mcp-scan (Invariant Labs): https://github.com/invariantlabs-ai/mcp-scan
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- Invariant Labs tool-poisoning disclosure: https://invariantlabs.ai/blog/introducing-mcp-scan
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- OWASP MCP Top 10: https://owasp.org/www-project-mcp-top-10/
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- Model Context Protocol spec: https://modelcontextprotocol.io/
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- MITRE ATLAS: https://atlas.mitre.org/
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- NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework
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