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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.
2.3 KiB
2.3 KiB
Model Extraction Detection — API / Library Reference
Libraries
| Library | Install | Purpose |
|---|---|---|
| adversarial-robustness-toolbox | pip install adversarial-robustness-toolbox |
Extraction, inversion, and membership-inference attacks + defenses |
| scikit-learn | pip install scikit-learn |
Surrogate / attack model training |
| numpy | pip install numpy |
Confidence-vector math, perturbation |
ART Extraction Attacks (art.attacks.extraction)
| Class | Key params | Purpose |
|---|---|---|
KnockoffNets |
nb_stolen, batch_size_query, nb_epochs, sampling_strategy |
Train surrogate from black-box queries (Knockoff Nets) |
CopycatCNN |
nb_stolen, batch_size_fit, batch_size_query |
Copycat surrogate extraction for neural nets |
attack.extract(x, thief_classifier=...) |
— | Run extraction; returns trained surrogate classifier |
ART Inference Attacks (art.attacks.inference.membership_inference)
| Class | Key methods | Purpose |
|---|---|---|
MembershipInferenceBlackBox |
.fit(...), .infer(x, y) |
Black-box membership inference (AML.T0024.000) |
MembershipInferenceBlackBoxRuleBased |
.infer(x, y) |
Rule-based MIA baseline (no shadow training) |
ART Defenses (postprocessors)
| Class | Purpose |
|---|---|
art.defences.postprocessor.ReverseSigmoid |
Perturb output probabilities to hinder extraction |
art.defences.postprocessor.Rounded |
Round confidence values to reduce leaked precision |
art.defences.postprocessor.HighConfidence |
Suppress low-confidence outputs |
Estimator Wrappers
| Class | Purpose |
|---|---|
art.estimators.classification.SklearnClassifier |
Wrap a scikit-learn model as an ART victim |
art.estimators.classification.KerasClassifier / PyTorchClassifier |
Wrap DL models |
Detection Signals (custom)
| Signal | Heuristic |
|---|---|
| Query volume | Queries/principal/window above baseline |
| Unique-input ratio | unique(input_hash)/queries → ~1.0 |
| Confidence-request ratio | Fraction of calls demanding full probability vectors |
External References
- ART docs: https://adversarial-robustness-toolbox.readthedocs.io/
- MITRE ATLAS AML.T0024: https://atlas.mitre.org/techniques/AML.T0024