# Whose Narrative is it Anyway? A KV Cache Manipulation Attack

arXiv: https://arxiv.org/abs/2511.12752 · HTML: https://mlsanity.com/paper/2511.12752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving.
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Placed in

- Efficient LLM inference and serving / [KV cache optimization](https://mlsanity.com/inference/kv-cache-optimization/)

## Builds on (most cited first, 6 of 6)

- [KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://mlsanity.com/paper/2401.18079/) (2024)
- [GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM](https://mlsanity.com/paper/2403.05527/) (2024)
- [LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference](https://mlsanity.com/paper/2510.09665/) (2025)
- [Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference](https://mlsanity.com/paper/2508.08438/) (2025)
- [Steering LLMs for Formal Theorem Proving](https://mlsanity.com/paper/2502.15507/) (2025)
- [Can Transformer Memory Be Corrupted? Investigating Cache-Side Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2510.17098/) (2025)

## Built on by (most cited first, 3 of 3)

- [HijackKV: New Threat in Position-Independent KV Cache Reuse](https://mlsanity.com/paper/2607.19957/) (2026)
- [Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems](https://mlsanity.com/paper/2604.17249/) (2026)
- [Probing the Prompt KV Cache: Where It Becomes Dispensable](https://mlsanity.com/paper/2605.30574/) (2026)

## Abstract (arXiv)

The Key Value(KV) cache is an important component for efficient inference in autoregressive Large Language Models (LLMs), but its role as a representation of the model's internal state makes it a potential target for integrity attacks. This paper introduces "History Swapping," a novel block-level attack that manipulates the KV cache to steer model generation without altering the user-facing prompt. The attack involves overwriting a contiguous segment of the active generation's cache with a precomputed cache from a different topic. We empirically evaluate this method across 324 configurations on the Qwen 3 family of models, analyzing the impact of timing, magnitude, and layer depth of the cache overwrite. Our findings reveal that only full-layer overwrites can successfully hijack the conversation's topic, leading to three distinct behaviors: immediate and persistent topic shift, partial recovery, or a delayed hijack. Furthermore, we observe that high-level structural plans are encoded early in the generation process and local discourse structure is maintained by the final layers of the model. This work demonstrates that the KV cache is a significant vector for security analysis, as it encodes not just context but also topic trajectory and structural planning, making it a powerful interface for manipulating model behavior.