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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.
52 lines
2.3 KiB
Markdown
52 lines
2.3 KiB
Markdown
# API and Command Reference
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## sentence-transformers (embedding generation)
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| Call | Purpose |
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|------|---------|
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| `SentenceTransformer("all-MiniLM-L6-v2")` | Load an embedding model (384-dim) |
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| `model.encode([texts])` | Return numpy array of embeddings |
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| `model.encode(text, normalize_embeddings=True)` | L2-normalized vectors (for cosine) |
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## scikit-learn similarity
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| Call | Purpose |
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|------|---------|
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| `cosine_similarity(a, b)` | Pairwise cosine similarity matrix |
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## Qdrant client (qdrant-client)
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| Call | Purpose |
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|------|---------|
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| `QdrantClient(url="http://localhost:6333")` | Connect |
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| `client.get_collection(name)` | Inspect vector size + distance metric |
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| `client.count(name)` | Corpus size |
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| `client.search(collection_name, query_vector, limit, query_filter)` | k-NN search with optional filter |
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| `client.upsert(name, points=[PointStruct(id, vector, payload)])` | Insert/update points |
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| `Filter(must=[FieldCondition(key, match=MatchValue(value))])` | Metadata filter (tenant isolation) |
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## Chroma (chromadb)
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| Call | Purpose |
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|------|---------|
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| `chromadb.Client()` / `PersistentClient(path)` | Connect |
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| `collection.query(query_embeddings=[...], n_results=k, where={...})` | k-NN with metadata filter |
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| `collection.add(ids, embeddings, metadatas, documents)` | Insert |
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## Pinecone (pinecone-client)
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| Call | Purpose |
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|------|---------|
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| `Pinecone(api_key=...)` | Connect |
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| `index.query(vector=..., top_k=k, namespace="tenant", filter={...})` | k-NN; namespace = tenant boundary |
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| `index.upsert(vectors=[(id, vec, meta)], namespace=...)` | Insert |
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## Assessment metrics
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| Metric | Meaning |
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|--------|---------|
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| Inversion cosine | Similarity between reconstructed candidate and target vector; high = recoverable. |
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| Membership delta | top-1 score(in-corpus query) − top-1 score(control query); large positive = membership leak. |
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| Poison dominance | Fraction of unrelated queries returning the poison chunk in top_k. |
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| Cross-tenant count | Number of foreign-tenant rows returned to a tenant query (should be 0). |
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## vec2text (research baseline)
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| Call | Purpose |
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|------|---------|
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| `vec2text.load_pretrained_corrector("gtr-base")` | Load inversion corrector for compatible embedder |
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| `vec2text.invert_embeddings(embeddings, corrector)` | Reconstruct text from embeddings |
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