Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks
Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-served models. Therefore, the safety of LLMs depends on the defense mechanisms used, and their effectiveness depends on the assumptions on which they were designed. This paper does an audit of defense mechanisms under jailbreak attacks on locally deployed models. Some defenses provide formal guarantees (SmoothLLM, Erase-and-Check, Sequential Monitors), while others rely on empirical detection results (Semantic Smoothing, Self-Denoised Smoothing, Perplexity Filtering). Instead of merely observing that defenses fail, we trace each failure back to t...
| Authors | Aaditya Pratap, Harsh Kasyap, Somanath Tripathy |
|---|---|
| Primary category | cs.CR |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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