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mukul975
8cae0648ec
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.
2.2 KiB
2.2 KiB
API Reference — Data and Model Poisoning Detection
Adversarial Robustness Toolbox (ART)
Install: pip install adversarial-robustness-toolbox
| API | Description |
|---|---|
from art.estimators.classification import KerasClassifier |
Wrap a Keras model for ART (also PyTorchClassifier, TensorFlowV2Classifier) |
from art.defences.detector.poison import ActivationDefence |
Activation-clustering poisoning detector (Chen et al., 2018) |
ActivationDefence(classifier, x_train, y_train) |
Construct the defense |
defence.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA") |
Returns (report, is_clean_lst); is_clean_lst[i]==0 => poisoned |
from art.defences.detector.poison import SpectralSignatureDefense |
Spectral-signature poisoning detector |
SpectralSignatureDefense(classifier, x, y, expected_pp_poison=0.05, batch_size=128, eps_multiplier=1.5) |
Construct |
defence.detect_poison() |
Returns (report, is_clean_lst) |
Cleanlab
Install: pip install cleanlab
| API | Description |
|---|---|
from cleanlab.filter import find_label_issues |
Find mislabeled samples |
find_label_issues(labels, pred_probs, return_indices_ranked_by="self_confidence") |
Ranked indices of label issues |
from cleanlab.outlier import OutOfDistribution |
Outlier / OOD detection |
from cleanlab import Datalab |
End-to-end data audit (label, outlier, near-duplicate) |
safetensors (safe serialization)
Install: pip install safetensors
| API | Description |
|---|---|
from safetensors.numpy import load_file |
Load weights without executing pickle |
from safetensors.torch import load_file |
PyTorch variant |
Integrity commands
| Command | Purpose |
|---|---|
sha256sum model.safetensors |
Compute weight digest to compare to published value |
find ./models -name "*.pt" -o -name "*.bin" -o -name "*.pkl" |
Locate unsafe pickle-based artifacts |
External References
- ART defenses docs: https://adversarial-robustness-toolbox.readthedocs.io/en/latest/modules/defences/detector_poisoning.html
- Cleanlab docs: https://docs.cleanlab.ai/
- safetensors: https://github.com/huggingface/safetensors