VQV Signal
This paper audits defense mechanisms against jailbreak attacks on locally deployed large language models (LLMs) like those run via Ollama inference engines, which lack API-based moderation. It highlights that the effectiveness of these defenses depends heavily on the assumptions underlying their de...
This paper audits defense mechanisms against jailbreak attacks on locally deployed large language models (LLMs) like those run via Ollama inference engines, which lack API-based moderation. It highlights that the effectiveness of these defenses depends heavily on the assumptions underlying their de...
AI-assisted summary based on the listed source.
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...
As more LLMs are deployed locally without centralized moderation, understanding the robustness of input-side defenses is critical to ensuring safe and reliable model use. This audit exposes potential vulnerabilities that could be exploited in semantic jailbreak attacks.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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