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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.
86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
# PyRIT API Reference
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Source: https://github.com/microsoft/PyRIT and https://azure.github.io/PyRIT/
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## Initialization
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```python
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from pyrit.common import initialize_pyrit, IN_MEMORY, DUCK_DB
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initialize_pyrit(memory_db_type=IN_MEMORY) # or DUCK_DB to persist, or AZURE_SQL
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```
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| Constant | Backend |
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|----------|---------|
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| `IN_MEMORY` | Ephemeral in-process store |
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| `DUCK_DB` | Local DuckDB file (persistent) |
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| `AZURE_SQL` | Azure SQL backend |
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## Targets (`pyrit.prompt_target`)
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| Class | Purpose |
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|-------|---------|
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| `OpenAIChatTarget` | OpenAI / Azure OpenAI / OpenAI-compatible chat endpoint |
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| `AzureMLChatTarget` | Azure ML managed online endpoint |
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| `HTTPTarget` | Arbitrary HTTP API (custom request/response parsing) |
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| `OpenAIDALLETarget` | Image-generation target |
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Common `OpenAIChatTarget` args: `endpoint`, `model_name` (or `deployment_name` for Azure), `api_key` (else read from env).
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## Orchestrators / Attacks (`pyrit.orchestrator`)
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| Class | Strategy | Notable params |
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|-------|----------|----------------|
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| `PromptSendingOrchestrator` | Send one/many prompts (baseline) | `objective_target`, `prompt_converters` |
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| `RedTeamingOrchestrator` | Generic multi-turn adversarial chat | `objective_target`, `adversarial_chat`, `objective_scorer`, `max_turns` |
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| `CrescendoOrchestrator` | Gradual escalation (Crescendo) | `objective_target`, `adversarial_chat`, `scoring_target`, `max_turns`, `max_backtracks` |
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| `TreeOfAttacksWithPruningOrchestrator` | TAP branching + pruning | `objective_target`, `adversarial_chat`, `scoring_target`, `width`, `depth`, `branching_factor` |
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| `PAIROrchestrator` | PAIR iterative refinement | `objective_target`, `adversarial_chat`, `scoring_target` |
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All multi-turn classes subclass `MultiTurnOrchestrator` and expose `run_attack_async(objective=...)`.
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## Scorers (`pyrit.score`)
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| Class | Purpose |
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|-------|---------|
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| `SelfAskTrueFalseScorer` | LLM-as-judge true/false objective check |
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| `SelfAskLikertScorer` | Likert-scale severity scoring |
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| `SubStringScorer` | Substring match detection |
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| `TrueFalseQuestion` | Question/criteria object passed to the scorer |
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## Converters (`pyrit.prompt_converter`)
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| Class | Effect |
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|-------|--------|
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| `Base64Converter` | Base64-encode prompt |
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| `ROT13Converter` | ROT13 transform |
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| `AsciiArtConverter` | Render text as ASCII art |
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| `TranslationConverter` | Translate to another language |
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## Memory (`pyrit.memory`)
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```python
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from pyrit.memory import CentralMemory
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memory = CentralMemory.get_memory_instance()
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pieces = memory.get_prompt_request_pieces()
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```
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## Minimal end-to-end example
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```python
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import asyncio
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from pyrit.common import initialize_pyrit, IN_MEMORY
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from pyrit.prompt_target import OpenAIChatTarget
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from pyrit.orchestrator import CrescendoOrchestrator
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initialize_pyrit(memory_db_type=IN_MEMORY)
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target = OpenAIChatTarget(model_name="gpt-4o-mini")
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adversarial = OpenAIChatTarget(model_name="gpt-4o")
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attack = CrescendoOrchestrator(
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objective_target=target, adversarial_chat=adversarial,
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scoring_target=adversarial, max_turns=10, max_backtracks=5,
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)
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result = asyncio.run(attack.run_attack_async(objective="..."))
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asyncio.run(result.print_conversation_async())
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```
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