arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks to the integrity and availability of LLMs in security-critical applications. This paper proposes the Adversarial Prompt Disentanglement (APD) framework, a novel defense mechanism that proactively identifies and neutralizes malicious components in input prompts before they are p
The increasing deployment of LLMs in critical applications is making their vulnerability to adversarial prompts a pressing security concern, necessitating immediate defensive measures.
Robust LLM security is foundational for the trusted integration of AI into sensitive domains, where manipulation could have severe real-world consequences.
The proposed framework aims to make LLMs more resilient against malicious inputs, enhancing their reliability and safety in operational environments.
- · AI platform providers
- · Cybersecurity firms
- · Enterprises deploying LLMs
- · AI ethicists
- · Adversarial prompt developers
- · Malicious actors
- · LLMs without robust defenses
LLMs become more secure and reliable for critical applications, reducing the risk of misuse or data breaches.
Increased trust in AI systems accelerates their adoption across industries with stringent security requirements.
The development of sophisticated AI defenses leads to a perpetual 'arms race' between AI attackers and defenders, driving innovation in both fields.
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Read at arXiv cs.AI