BadEngram: Backdoor Attack on Gated Memory Components in LLMs

Abstract

To expand open-weight models'capacity without proportionally increasing computation, recent architectures incorporate gated parametric memory that retrieves learned values and injects them into intermediate representations. One representative design is Engram, which combines deterministic n-gram lookup with context-dependent gating over large learned memory tables. Despite these efficiency benefits, such modules create a distinct attack surface: their parameters can be modified independently of the backbone while directly shaping its computation. We introduce BadEngram, a post-training attack that exploits this surface to implant persistent, trigger-dependent behavior while leaving conventional backbone weights and the execution graph unchanged. We first establish the attack's feasibility and causally characterize its mechanism in a controlled Engram model, where BadEngram achieves 96.6% ASR on triggered inputs while limiting false activation on matched trigger-free inputs to 0.1% and preserving 99.6% clean accuracy. Replacing the retrieved memory values with their clean counterparts or closing the memory gates reduces ASR to at most 0.32%, confirming that the backdoor is expressed through the gated-memory pathway. We then test whether this vulnerability extends to production scale in Qwen3.8-Flash-Next's native Per-Layer Embedding subsystem. Using independently trained checkpoints for the two benchmarks, BadEngram achieves 47.8% ASR on HarmBench and 64.0% on AdvBench, while dormant-condition ASR remains 0.9% and 0.0%, respectively. These results identify native gated-memory parameters as a security-critical part of the model whose integrity cannot be inferred from an unchanged backbone.