arXiv:2605.29979v1 Announce Type: cross Abstract: The behavior of LLMs does not depend solely on the model itself. Components of the inference system, such as the inference engine, attention backend, and hardware platform, subtly influence how inputs are processed. These components differ in their implementations and thereby induce small numerical deviations across systems when running the same model. While prior work has established the theoretical existence of such deviations, their security implications have remained unexplored. In this paper, we show that these deviations are characteristi
The proliferation of various LLM inference systems makes identifying unique system fingerprints a current and pressing security and intellectual property concern.
This research reveals new attack vectors and attribution methods within LLM ecosystems, impacting security, intellectual property, and competitive intelligence.
The ability to fingerprint LLM inference systems means that the 'black box' of model execution is becoming more transparent, enabling new forms of analysis and potentially exploitation.
- · Cybersecurity firms
- · LLM security researchers
- · Intellectual property rights holders
- · Malicious actors
- · Organizations with inadequate LLM inference system security
- · Model cloners
Identification of LLM inference system components becomes a viable method for attribution and security analysis.
New security products and services will emerge focusing on protecting LLM inference system integrity and preventing fingerprinting.
The development of 'fingerprint-resistant' inference systems could become a competitive advantage, leading to an arms race in LLM infrastructure security.
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Read at arXiv cs.LG