arXiv:2606.19535v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sensitive settings such as software engineering, where their outputs directly shape downstream artifacts. Recent work has shown that an identical model can produce measurably different outputs depending on the deployment platform, a consequence of non-associative floating-point arithmetic and divergent kernel implementations. We study the security implications of this platform-dependent variability and uncover a novel attack surface on LLM deployments. We introduce FloatDoor, the first i
The increasing deployment of LLMs in sensitive and critical environments makes their platform-dependent security vulnerabilities an immediate concern for both developers and users.
This research reveals a novel attack surface on LLM deployments, highlighting that even identical models can be compromised due to subtle, platform-specific computational differences.
The understanding of LLM security expands beyond traditional model vulnerabilities to include the infrastructure and computational environments they run on.
- · Cybersecurity firms
- · Cloud platform providers with robust security
- · Adversarial AI researchers
- · LLM deployers
- · Software engineering firms relying on LLMs
- · Open-source LLM platforms
Immediate patching and security updates will be required for LLM deployment platforms.
Increased scrutiny and standardization efforts for LLM runtime environments and their underlying computational guarantees will emerge.
New regulatory frameworks may arise, mandating specific security testing and compliance for AI systems based on their deployment architecture.
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Read at arXiv cs.LG