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Process mining reveals LLM red teaming defense differences

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method using process mining to analyze how Large Language Models (LLMs) respond to red teaming attacks. This approach moves beyond simple success/fail metrics to examine the sequential interactions during an attack. Experiments with GPT-OSS 120B and Llama 3.3 70B revealed distinct defense patterns, showing GPT-OSS quickly entering a refusal state while Llama had multiple pathways to being jailbroken. AI

IMPACT Introduces a novel analytical framework for understanding LLM vulnerabilities beyond simple pass/fail metrics.

RANK_REASON The cluster contains an academic paper detailing a new methodology for evaluating LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Process mining reveals LLM red teaming defense differences

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The cluster contains an academic paper detailing a new methodology for evaluating LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zvi Topol ·

    Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (and Fail) Red Team Attacks

    arXiv:2606.07833v1 Announce Type: cross Abstract: Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate (ASR), not taking into account the sequential structure of how models resist or yield to attacks. We propose applying…

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