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PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System

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arxiv 2410.02828 v1 pith:MS46RE6H submitted 2024-10-01 cs.CR cs.AIcs.CL

classification cs.CRcs.AIcs.CL
keywords generativepyritgenaiidentificationmodelsriskteamingframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the need for extensible and model-agnostic risk identification frameworks is growing. To meet this need, we introduce the Python Risk Identification Toolkit (PyRIT), an open-source framework designed to enhance red teaming efforts in GenAI systems. PyRIT is a model- and platform-agnostic tool that enables red teamers to probe for and identify novel harms, risks, and jailbreaks in multimodal generative AI models. Its composable architecture facilitates the reuse of core building blocks and allows for extensibility to future models and modalities. This paper details the challenges specific to red teaming generative AI systems, the development and features of PyRIT, and its practical applications in real-world scenarios.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs

    cs.CR 2026-07 conditional novelty 7.0 of 10

    With copyable pre-release evidence, any dual-use release rule that keeps legitimate utility q must leave worst-case attacker assistance at least Γ(q)>0, so useful capability, reliable safety, and open access cannot coexist.

  2. AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation

    cs.CR 2026-07 conditional novelty 6.0 of 10

    A phase-structured multi-turn red-team framework reports 97.6–100% lenient ASR but only 66.7–78.6% full actionable ASR on six frontier LLMs, with success strongly depth-dependent.

  3. A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks

    cs.CR 2025-06 conditional novelty 4.0 of 10

    Crescendo multi-turn jailbreak responses are represented by safety-tuned LLMs as benign rather than harmful, which helps explain why single-turn defenses fail.

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