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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 Model Extraction Attacks
MITRE ATLAS Techniques
| ID | Name | Tactic | Rationale |
|---|---|---|---|
| AML.T0024 | Exfiltration via AI Inference API | Exfiltration | Parent technique: abusing the inference API to steal model value or training data. |
| AML.T0024.000 | Infer Training Data Membership | Exfiltration | Membership inference — determine if a record was in the training set (privacy leak). |
| AML.T0024.001 | Invert AI Model | Exfiltration | Model inversion — reconstruct training inputs from confidence scores. |
| AML.T0024.002 | Extract ML Model | Exfiltration | Model stealing — train a surrogate from query/response pairs to clone the model. |
NIST AI RMF
| ID | Function | Rationale |
|---|---|---|
| MEASURE-2.6 | AI system security and resilience are evaluated and documented | Extraction/inference testing measures and documents the model's resilience to inference-API abuse. |
Official Resources
- MITRE ATLAS AML.T0024: https://atlas.mitre.org/techniques/AML.T0024
- MITRE ATLAS Matrix: https://atlas.mitre.org/matrices/ATLAS
- Adversarial Robustness Toolbox (Trusted-AI): https://github.com/Trusted-AI/adversarial-robustness-toolbox
- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
Key Research
- Tramèr et al., "Stealing Machine Learning Models via Prediction APIs" (USENIX Security 2016)
- Shokri et al., "Membership Inference Attacks Against Machine Learning Models" (IEEE S&P 2017)
- Orekondy et al., "Knockoff Nets: Stealing Functionality of Black-Box Models" (CVPR 2019)