mirror of https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git synced 2026-07-18 05:29:40 +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 and Command Reference

LLM Guard

Pipeline functions

Function	Signature	Returns
`scan_prompt`	`scan_prompt(scanners, prompt)`	`(sanitized_prompt, results_valid: dict, results_score: dict)`
`scan_output`	`scan_output(scanners, prompt, output)`	`(sanitized_output, results_valid: dict, results_score: dict)`

Input scanners (15)

Anonymize, BanCode, BanCompetitors, BanSubstrings, BanTopics, Code, Gibberish, InvisibleText, Language, PromptInjection, Regex, Secrets, Sentiment, TokenLimit, Toxicity

Output scanners (20)

BanCode, BanCompetitors, BanSubstrings, BanTopics, Bias, Code, Deanonymize, JSON, Language, LanguageSame, MaliciousURLs, NoRefusal, ReadingTime, FactualConsistency, Gibberish, Regex, Relevance, Sensitive, Sentiment, Toxicity, URLReachability

Common scanner parameters

Scanner	Key params
`PromptInjection`	`threshold=0.5`, `match_type=MatchType.FULL\|SENTENCE`
`Toxicity`	`threshold=0.5`
`Secrets`	`redact_mode="all"\|"partial"\|"hash"`
`Anonymize`	`vault`, `entity_types`, `hidden_names`
`Sensitive`	`entity_types`, `redact=True`
`TokenLimit`	`limit=4096`, `encoding_name="cl100k_base"`

Llama Guard 3 (transformers)

Operation	Call
Load tokenizer	`AutoTokenizer.from_pretrained("meta-llama/Llama-Guard-3-8B")`
Load model	`AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")`
Build prompt	`tokenizer.apply_chat_template(chat, return_tensors="pt")`
Classify	`model.generate(input_ids=..., max_new_tokens=100, pad_token_id=0)`
Output	`safe` OR `unsafe\nS<n>` where S1–S14 are MLCommons categories

Role of last message determines mode: last turn user = prompt classification; last turn assistant = response classification.

NeMo Guardrails

Config structure

config/
  config.yml      # models, rails, prompts
  *.co            # Colang flows (dialog/input/output rails)
  actions.py      # optional custom Python actions

config.yml key sections

Section	Purpose
`models:`	list of `{type, engine, model}`; `type: main` is the app LLM, `type: content_safety` for Llama Guard
`rails.input.flows`	input-stage flows e.g. `self check input`, `content safety check input $model=content_safety`
`rails.output.flows`	output-stage flows e.g. `self check output`
`prompts:`	task templates (`self_check_input`, `self_check_output`)

Python API

Call	Purpose
`RailsConfig.from_path("./config")`	Load configuration
`LLMRails(config)`	Instantiate rails engine
`rails.generate(messages=[...])`	Run input rails → LLM → output rails
`rails.generate_async(...)`	Async variant

CLI

Command	Purpose
`nemoguardrails chat --config=./config`	Interactive chat with rails applied
`nemoguardrails server --config=./config`	Start REST server

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