arXiv:2607.07903v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing approaches primarily analyze these failures through input-output behaviors or attribution methods, offering limited insight into how adversarial perturbations alter the model's internal reasoning. Consequently, the mechanisms underlying unsafe or incorrect behaviors remain poorly understood. We introduce a mechanistic framework for diagnosing LLM vulnerabilities using paired internal computation graphs
The proliferation of LLMs and their increasing deployment in sensitive applications makes understanding and mitigating their vulnerabilities to adversarial attacks a critical and urgent research area.
This research provides a deeper, mechanistic understanding of how LLMs fail, which is crucial for building more robust, secure, and trustworthy AI systems, particularly as AI agents become more prevalent.
The ability to 'diagnose' LLM vulnerabilities via internal computation graphs moves beyond simple input-output analysis, allowing for more targeted and effective defense mechanisms and improved model safety.
- · AI developers
- · Cybersecurity researchers
- · Organizations deploying LLMs
- · Adversarial attackers
- · Malicious actors
- · Unsophisticated LLM deployments
Improved understanding of LLM vulnerabilities will lead to the development of more resilient AI models.
Enhanced LLM security could accelerate the adoption of AI agents in more critical and high-stakes applications.
A fundamental breakthrough in explainable AI could emerge from this mechanistic interpretability, fostering greater public trust and regulatory acceptance of advanced AI.
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Read at arXiv cs.AI