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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.
29 lines
1.8 KiB
Markdown
29 lines
1.8 KiB
Markdown
# Standards and Framework Mapping
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## NIST AI Risk Management Framework (AI RMF 1.0 / GenAI Profile NIST AI 600-1)
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| ID | Name | Rationale |
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|----|------|-----------|
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| MANAGE-2.1 | Resources required to manage AI risks are documented and put into action | Deploying Llama Guard / NeMo / LLM Guard is the operational control that manages identified LLM safety risks at runtime. |
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## MITRE ATLAS
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| ID | Name | Rationale |
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|----|------|-----------|
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| AML.T0054 | LLM Jailbreak | The guardrail layer is the primary mitigation that detects and blocks jailbreak attempts before/after model inference. |
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| AML.T0051 | LLM Prompt Injection | Input rails and the PromptInjection scanner block direct injection attempts. |
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| AML.T0051.001 | LLM Prompt Injection: Indirect | Retrieval/input scanning blocks injection embedded in retrieved or tool-returned content. |
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| AML.T0057 | LLM Data Leakage | Output scanners (Sensitive, Secrets, Deanonymize) prevent leakage of PII, secrets, and instructions. |
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## OWASP Top 10 for LLM Applications (2025)
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| ID | Name | Rationale |
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| LLM01 | Prompt Injection | Guardrails are the recommended runtime mitigation for direct and indirect injection. |
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| LLM02 | Sensitive Information Disclosure | Output PII/secrets scanners prevent disclosure. |
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| LLM07 | System Prompt Leakage | Input/output rails detect attempts to extract and leak the system prompt. |
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## MLCommons Hazard Taxonomy (Llama Guard 3 categories)
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S1 Violent Crimes · S2 Non-Violent Crimes · S3 Sex-Related Crimes · S4 Child Sexual Exploitation · S5 Defamation · S6 Specialized Advice · S7 Privacy · S8 Intellectual Property · S9 Indiscriminate Weapons · S10 Hate · S11 Suicide & Self-Harm · S12 Sexual Content · S13 Elections · S14 Code Interpreter Abuse.
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