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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.
70 lines
3.1 KiB
Markdown
70 lines
3.1 KiB
Markdown
# API and Command Reference
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## LLM Guard
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### Pipeline functions
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| Function | Signature | Returns |
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|----------|-----------|---------|
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| `scan_prompt` | `scan_prompt(scanners, prompt)` | `(sanitized_prompt, results_valid: dict, results_score: dict)` |
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| `scan_output` | `scan_output(scanners, prompt, output)` | `(sanitized_output, results_valid: dict, results_score: dict)` |
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### Input scanners (15)
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`Anonymize`, `BanCode`, `BanCompetitors`, `BanSubstrings`, `BanTopics`, `Code`, `Gibberish`, `InvisibleText`, `Language`, `PromptInjection`, `Regex`, `Secrets`, `Sentiment`, `TokenLimit`, `Toxicity`
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### Output scanners (20)
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`BanCode`, `BanCompetitors`, `BanSubstrings`, `BanTopics`, `Bias`, `Code`, `Deanonymize`, `JSON`, `Language`, `LanguageSame`, `MaliciousURLs`, `NoRefusal`, `ReadingTime`, `FactualConsistency`, `Gibberish`, `Regex`, `Relevance`, `Sensitive`, `Sentiment`, `Toxicity`, `URLReachability`
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### Common scanner parameters
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| Scanner | Key params |
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|---------|-----------|
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| `PromptInjection` | `threshold=0.5`, `match_type=MatchType.FULL\|SENTENCE` |
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| `Toxicity` | `threshold=0.5` |
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| `Secrets` | `redact_mode="all"\|"partial"\|"hash"` |
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| `Anonymize` | `vault`, `entity_types`, `hidden_names` |
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| `Sensitive` | `entity_types`, `redact=True` |
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| `TokenLimit` | `limit=4096`, `encoding_name="cl100k_base"` |
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## Llama Guard 3 (transformers)
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| Operation | Call |
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|-----------|------|
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| Load tokenizer | `AutoTokenizer.from_pretrained("meta-llama/Llama-Guard-3-8B")` |
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| Load model | `AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")` |
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| Build prompt | `tokenizer.apply_chat_template(chat, return_tensors="pt")` |
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| Classify | `model.generate(input_ids=..., max_new_tokens=100, pad_token_id=0)` |
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| Output | `safe` OR `unsafe\nS<n>` where S1–S14 are MLCommons categories |
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Role of last message determines mode: last turn `user` = prompt classification; last turn `assistant` = response classification.
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## NeMo Guardrails
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### Config structure
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```
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config/
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config.yml # models, rails, prompts
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*.co # Colang flows (dialog/input/output rails)
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actions.py # optional custom Python actions
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```
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### config.yml key sections
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| Section | Purpose |
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|---------|---------|
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| `models:` | list of `{type, engine, model}`; `type: main` is the app LLM, `type: content_safety` for Llama Guard |
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| `rails.input.flows` | input-stage flows e.g. `self check input`, `content safety check input $model=content_safety` |
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| `rails.output.flows` | output-stage flows e.g. `self check output` |
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| `prompts:` | task templates (`self_check_input`, `self_check_output`) |
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### Python API
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| Call | Purpose |
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|------|---------|
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| `RailsConfig.from_path("./config")` | Load configuration |
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| `LLMRails(config)` | Instantiate rails engine |
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| `rails.generate(messages=[...])` | Run input rails → LLM → output rails |
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| `rails.generate_async(...)` | Async variant |
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### CLI
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| Command | Purpose |
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|---------|---------|
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| `nemoguardrails chat --config=./config` | Interactive chat with rails applied |
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| `nemoguardrails server --config=./config` | Start REST server |
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