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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.
1.6 KiB
1.6 KiB
Standards and References — Detecting Data and Model Poisoning
MITRE ATLAS References
| Technique ID | Name | Tactic | Rationale |
|---|---|---|---|
| AML.T0020 | Poison Training Data | ML Attack Staging | Injection of manipulated samples into the training corpus |
| AML.T0018 | Backdoor ML Model | Persistence | Trigger-activated hidden behavior in the trained model |
| AML.T0010 | ML Supply Chain Compromise | Initial Access | Poisoned public datasets / trojaned downloaded weights |
| AML.T0024 | Exfiltration via ML Inference API | Exfiltration | Some poisoning leaks data via model responses |
NIST AI RMF References
| ID | Name | Rationale |
|---|---|---|
| MEASURE-2.7 | AI system security and resilience are evaluated and documented | Poisoning detection measures the integrity/resilience of the ML pipeline |
OWASP Top 10 for LLM Applications (2025)
| ID | Name | Rationale |
|---|---|---|
| LLM04:2025 | Data and Model Poisoning | Primary risk this skill detects |
| LLM03:2025 | Supply Chain | Trojaned weights/datasets entry path |
Official Resources
- Adversarial Robustness Toolbox: https://github.com/Trusted-AI/adversarial-robustness-toolbox
- ART poisoning defenses docs: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/defences/detector_poisoning.html
- Cleanlab: https://github.com/cleanlab/cleanlab
- safetensors: https://github.com/huggingface/safetensors
- OWASP LLM04:2025: https://genai.owasp.org/llmrisk/llm042025-data-and-model-poisoning/
- MITRE ATLAS: https://atlas.mitre.org/
- NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework