CacheTrap: Injecting Trojans in LLMs without Leaving any Traces in Inputs or Weights

• URL: http://arxiv.org/abs/2511.22681v1
• Date: Thu, 27 Nov 2025 18:30:19 GMT
• Title: CacheTrap: Injecting Trojans in LLMs without Leaving any Traces in Inputs or Weights
• Abstract summary: We introduce CacheTrap, a novel Trojan attack that corrupts the value vectors stored in the KV cache.<n>Our evaluation demonstrates that the proposed attack enables the first successful Trojan attack on LLMs with a single bit flip in the KV cache.
• Score: 14.467934380203873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial weight perturbation has emerged as a concerning threat to LLMs that either use training privileges or system-level access to inject adversarial corruption in model weights. With the emergence of innovative defensive solutions that place system- and algorithm-level checks and corrections in the input and weight spaces, these perturbations are increasingly susceptible to defenses. This work develops a novel perspective on Trojan attacks that generates an attacker-designed model output while leaving no attack traces on the inputs or weights. Such an attack space can be unlocked through corruption of the key-value (KV) cache. In this paper, we introduce CacheTrap, a novel Trojan attack that corrupts the value vectors stored in the KV cache. These vectors capture the dynamic activations for specific token positions and therefore constitute a natural surface for transient, inference-time trigger insertion. The transient nature of these KV values and their dependence on victim input imply additional constraints on our attack, such as a lack of knowledge of the victim's data or domain application, and, consequently, a lack of gradient information. The objective of the proposed CacheTrap is to develop a vulnerable KV bit-searching algorithm so that, once the attack employs the identified bit-flip as a trigger, the model generates targeted behavior, e.g., classifying inputs towards the target class. Moreover, CacheTrap is a data- and gradient-free attack which also has no impact on the model's utility. Our evaluation demonstrates that the proposed attack enables the first successful Trojan attack on LLMs with a single bit flip in the KV cache. In addition, the data-independent nature of the attack ensures that once the attacker identifies the vulnerable bit index, the location remains constant and can be transferred to a wide range of victim tasks/datasets/queries with no overhead.

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