HijackKV: New Threat in Position-Independent KV Cache Reuse
- URL: http://arxiv.org/abs/2607.19957v1
- Date: Wed, 22 Jul 2026 09:32:45 GMT
- Title: HijackKV: New Threat in Position-Independent KV Cache Reuse
- Abstract summary: Key-Value ( KV) cache reduces inference latency in large language models (LLMs)<n>We show this design introduces a new threat, KV Cache Hijacking.<n>We introduce HIJACKKV, the first attack framework that systematically exploits this vulnerability.
- Score: 18.183961370228833
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Key-Value (KV) cache reduces inference latency in large language models (LLMs). Traditional prefix-based reuse has low cache hit rates across inference requests because it requires exact token and position matches. To improve efficiency, recent system optimizations introduce position-independent KV reuse, allowing KV cache to be reused whenever identical text chunks appear, regardless of their position in the sequence. We show this design introduces a new threat, KV Cache Hijacking. Since KV caches are retrieved by token match but encode the context in which they were originally computed, the KV tied to a benign-looking token chunk may encode an attacker-controlled prefix. When later reused in a victim query, this contaminated KV silently hijacks the model's behavior, even if no attacker-controlled text appears in the input. We introduce HIJACKKV, the first attack framework that systematically exploits this vulnerability, demonstrating its severity and practicality. HIJACKKV optimizes an attacker-controlled prefix, so that the KV computed for a subsequent common benign text encodes the attacker's goal, while the text remains unchanged for future cache hits. HIJACKKV achieves an average 94% success rate in a single attempt, remains effective under realistic constraints including low hit rates (10%) and frequent recomputation (50%), persists over multi-turn interactions, and transfers across models in black-box settings. We further provide design insights for building secure KV reuse systems.
Related papers
- AnchorKV: Safety-Aware KV Cache Compression via Soft Penalty with a Refusal Anchor [20.7510970936151]
Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks.<n>Their large size introduces significant challenges in memory usage, energy cost, and on-device deployment.<n>We propose AnchorKV, a drop-in modification to KV cache compression that biases token retention scores away from directions in key space associated with harmful prompts.
arXiv Detail & Related papers (2026-06-16T12:43:18Z) - KARA: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression [3.9838043625801967]
Reasoning language models generate long chain-of-thought (CoT)<n>CoT accumulates a massive KV cache during the decoding phase.<n> KV cache compression has emerged as a promising technique for reducing memory overhead.
arXiv Detail & Related papers (2026-05-01T15:54:13Z) - DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity [50.52392445266824]
We propose a residual-based KV cache compression framework motivated by long-range inter-token similarity and highly shared latent components in KV representations.<n>Instead of discarding tokens, DeltaKV encodes semantic residuals relative to retrieved historical references, preserving fidelity while substantially reducing storage.<n>Experiments show that DeltaKV reduces KV cache memory to 29% of the original while maintaining near-lossless accuracy on LongBench, SCBench, and AIME.
arXiv Detail & Related papers (2026-02-08T15:14:36Z) - SpecAttn: Co-Designing Sparse Attention with Self-Speculative Decoding [3.47505166101801]
SpecAttn is a self-speculative decoding method with verification-guided sparse attention.<n>It achieves 2.81$times$ higher throughput over vanilla auto-regressive decoding and 1.29$times$ improvement over state-of-the-art sparsity-based decoding methods.
arXiv Detail & Related papers (2026-02-06T22:12:52Z) - KV-CAR: KV Cache Compression using Autoencoders and KV Reuse in Large Language Models [3.5171501100868876]
The KV cache grows with sequence length and embedding dimension, often exceeding the memory footprint of the model itself.<n>We present KV CAR, a unified and agnostic architecture framework that significantly reduces KV cache storage while maintaining model fidelity.<n> Evaluations on GPT 2 and TinyLLaMA models across Wikitext, C4, PIQA, and Winogrande datasets demonstrate that KV CAR achieves up to 47.85 percent KV cache memory reduction.
arXiv Detail & Related papers (2025-12-07T08:40:52Z) - KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction [20.53279247581787]
We propose KVReviver, a reversible KV cache compression method based on the sketch algorithm.<n>In 2k-length contexts, it requires only 10% of KV Cache budget while maintaining identical end-to-end inference accuracy.<n>For 32k-length contexts, it achieves equivalent or comparable accuracy 2% accuracy loss.
arXiv Detail & Related papers (2025-12-01T03:59:20Z) - Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inference [17.46930265810127]
Key-Value ( KV) cache stores intermediate attention computations (Key and Value pairs) to avoid redundant calculations.<n>This paper provides the first comprehensive analysis of vulnerabilities, demonstrating that an attacker can reconstruct sensitive user inputs directly from the KV-cache.<n>We propose KV-Cloak, a novel, lightweight, and efficient defense mechanism.
arXiv Detail & Related papers (2025-08-13T02:48:25Z) - Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs? [79.58770714228983]
Language models handle increasingly long contexts for tasks such as book summarization.<n>This leads to growing memory costs for the key-value ( KV) cache.<n>Many prior works have proposed ways of discarding KVs from memory, but their approaches are tailored to favorable settings.<n>We propose the * KV footprint* as a unified metric, which accounts for both the amount of KV entries stored and their lifespan in memory.
arXiv Detail & Related papers (2025-06-20T16:21:12Z) - R-KV: Redundancy-aware KV Cache Compression for Reasoning Models [77.84539432982307]
We propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV)<n>R-KV preserves nearly 100% of the full KV cache performance using only 10% of the KV cache.<n>Remarkably, R-KV even achieves 105% of full KV cache performance with 16% of the KV cache.
arXiv Detail & Related papers (2025-05-30T02:03:24Z) - FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management [48.904743679691414]
FlowKV is a novel multi-turn isolation mechanism for KV Cache management.<n>It preserves the accumulated compressed KV cache from past turns.<n>It prevents the re-compression of older context and thereby mitigating catastrophic forgetting.
arXiv Detail & Related papers (2025-05-21T10:20:46Z) - KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing [58.29726147780976]
We propose a plug-and-play method called textit KVSharer, which shares the KV cache between layers to achieve layer-wise compression.
Experiments show that textit KVSharer can reduce KV cache computation by 30%, thereby lowering memory consumption.
We verify that textit KVSharer is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
arXiv Detail & Related papers (2024-10-24T08:06:41Z) - PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling [38.732413451399]
Pyramid KV is a novel and effective KV cache compression method.<n>We show that Pyramid KV matches the performance of models with a full KV cache while retaining only 12% of the KV cache.<n>In the Needle-in-a-Haystack experiment, Pyramid KV outperforms competing methods in maintaining long-context comprehension.
arXiv Detail & Related papers (2024-06-04T07:51:30Z) - Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs [82.08922896531618]
We introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs)
We conduct targeted profiling to discern the intrinsic structure of attention modules.
Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens.
arXiv Detail & Related papers (2023-10-03T05:17:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.