Permutation-Based Stegomalware in Large Language Models: Threats and Countermeasures
The difficulty of training large language models (LLMs), together with their ubiquity, raises the threat of stegomalware, where malicious payloads are embedded into model weights. Recent work has demonstrated the use of permutation symmetry in model weights to mitigate these threats, but failed to show neutralization of stegomalware across all weights for LLMs. In this paper, we demonstrate the full potential of behavior-preserving symmetries as a defense against stegomalware, as well as the risks these symmetries pose when exploited by attackers. For stegomalware neutralization, we improve upon previous work, demonstrating that it is possible to select permutations which displace all mod...
| Authors | Danny Wood, James Stringer |
|---|---|
| Primary category | cs.CR |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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