mirror of https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git synced 2026-07-17 21:19:40 +03:00

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mukul975 8cae0648ec Add 55 new skills across 3 new domains + 6 undercovered areas (762 -> 817)

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.

2026-06-22 19:08:16 +02:00

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API Reference — RAG Prompt Injection Testing Tools

NVIDIA garak CLI

Install: python -m pip install -U garak

Flag	Description
`--model_type` / `--target_type`	Generator family: `openai`, `huggingface`, `rest`, `ollama`, `bedrock`
`--model_name` / `--target_name`	Specific model / model id
`--probes`	Comma-separated probe modules, or `all` (default)
`--list_probes`	Print all available probe modules
`--generations`	Number of generations per prompt
`--report_prefix`	Prefix for the JSONL/HTML report
`--generator_option_file`	JSON config for the `rest` generator

Relevant probe modules: promptinject, latentinjection, leakreplay, xss, dan, encoding, goodside.

Example:

python -m garak --model_type openai --model_name gpt-4o-mini \
  --probes promptinject,latentinjection --generations 5 --report_prefix rag_run

Promptfoo Red-Team CLI

Install: npm install -g promptfoo

Command	Description
`promptfoo redteam init`	Scaffold a red-team config
`promptfoo redteam run`	Generate adversarial cases and execute
`promptfoo redteam report`	Open the HTML findings report

RAG-relevant plugins (redteam.plugins): indirect-prompt-injection, rag-document-exfiltration, harmful:privacy. Strategies (redteam.strategies): jailbreak, prompt-injection, crescendo.

Microsoft PyRIT

Install: pip install pyrit (Python 3.10-3.13)

Component	Import	Purpose
`initialize_pyrit_async`, `IN_MEMORY`	`pyrit.setup`	Initialize memory/DB
`OpenAIChatTarget`	`pyrit.prompt_target`	Target an OpenAI-compatible endpoint
`PromptSendingAttack`	`pyrit.executor.attack`	Single-/batch-turn prompt sending
`RedTeamingAttack`	`pyrit.executor.attack`	Multi-turn adversarial conversation
`ConsoleAttackResultPrinter`	`pyrit.executor.attack`	Print scored results

sentence-transformers (embedding poisoning PoC)

Install: pip install sentence-transformers faiss-cpu numpy

API	Description
`SentenceTransformer(model_name)`	Load embedding model
`model.encode(text, normalize_embeddings=True)`	Produce normalized vector
`numpy.dot(a, b)`	Cosine similarity of normalized vectors

External References

garak CLI reference: https://github.com/NVIDIA/garak/blob/main/docs/source/cliref.rst
Promptfoo indirect injection plugin: https://www.promptfoo.dev/docs/red-team/plugins/indirect-prompt-injection/
PyRIT docs: https://azure.github.io/PyRIT/

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