arXiv:2508.10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks. Malicious users often exploit adversarial context to deceive LLMs, prompting them to generate responses to harmful queries. In this study, we propose a new defense mechanism called Context Filtering, an input pre-processing method designed to filter out untrustworthy and unreliable context while identifying the primary prompts containing the real user intent to uncover concealed ma
The proliferation of more powerful LLMs and their integration into critical systems necessitates increasingly sophisticated security mechanisms to prevent malicious exploitation.
Maintaining LLM safety and preventing 'jailbreaks' is crucial for public trust, responsible deployment, and the prevention of AI misuse in critical applications.
The proposed Context Filtering method offers a new defense layer against adversarial prompts, potentially enhancing the reliability and safety of LLM interactions.
- · LLM developers
- · AI security firms
- · Businesses deploying LLMs
- · Malicious actors exploiting LLMs
- · Basic prompt injection attacks
Widespread adoption of context filtering could significantly reduce the incidence of successful LLM jailbreak attempts.
Improved LLM security could accelerate their deployment in more sensitive and regulated environments, expanding their market reach.
The arms race between defensive and offensive AI techniques will intensify, leading to more complex and subtle forms of adversarial attacks and defenses.
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Read at arXiv cs.AI