Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
Effective safety auditing of large language models (LLMs) demands tools that go beyond black-box probing and systematically uncover vulnerabilities rooted in model internals. We present a comprehensive, interpretability-driven jailbreaking audit of eight SOTA open-source LLMs: Llama-3.1-8B, Llama-3.3-70B-4bt, GPT-oss- 20B, GPT-oss-120B, Qwen3-0.6B, Qwen3-32B, Phi4-3.8B, and Phi4-14B. Leveraging interpretability-based approaches -- Universal Steering (US) and Representation Engineering (RepE) -- we introduce an adaptive two-stage grid search algorithm to identify optimal activation-steering coefficients for unsafe behavioral concepts. Our evaluation, conducted on a curated set of harmful q...
| Authors | Krishiv Agarwal, Ramneet Kaur, Colin Samplawski et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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