arXiv:2605.20641v1 Announce Type: cross Abstract: Inference optimization is a vital technique for deploying LLMs at scale. Compilation is the most widely adopted optimization technique for LLMs. While it assumes semantic equivalence between the original and compiled graphs, we first uncover its numerical side effects can be maliciously exploited to implant stealthy backdoors in LLMs. We propose a unified optimization-triggered attack framework comprising two complementary strategies. Without any modification to the compiler or hardware, one strategy flips predictions for specific inputs only w
The increasing deployment of LLMs at scale, coupled with a focus on optimization for efficiency, creates new vulnerabilities that researchers are actively exploring.
This research highlights a novel and stealthy attack vector against large language models, potentially compromising their integrity and trustworthiness in sensitive applications, impacting national security and enterprise trust.
The understanding of LLM security expands beyond traditional adversarial attacks to include vulnerabilities introduced during optimization and compilation, requiring new defense strategies and validation processes.
- · Cybersecurity researchers
- · LLM security solution providers
- · Organizations prioritizing robust AI security
- · LLM deployers without robust validation
- · Organizations reliant on untrustworthy LLM inferences
- · Cloud providers without enhanced security protocols
Immediate industry focus on securing LLM compilation and optimization pipelines.
Development of new security standards and audit requirements for AI model deployment, particularly in critical infrastructure.
Potential for 'AI supply chain attacks' becoming a significant national security concern, leading to governmental intervention and regulation in AI development and deployment.
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