Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.24872

The Missing Compliance Class: LLM Graders That Collapse Jailbreak Success, and a Free Check

Ruqiya Bin Safi
View original source ↗Read source document ↗Export metadata ↓

Abstract

Jailbreak papers report an attack success rate, the share of responses that deliver the harmful request, and read it as a property of the target model. We hold the target model, the prompts and the responses fixed and change only the grader. Three dense graders of 8B and 9B parameters, from three model families, move compliant responses to the neighbouring class, evasion, instead of scattering them across classes. One of them assigns the compliance label zero times in 900 responses. This class collapse also appears on new harmful requests, on a target model from a third family, and in a held-out 7B grader from a fourth family. Per-class label counts expose it at no cost and without human labels. The collapse decides how the rate behaves under a change of grader. On two corpora of 900 responses and eight graders, the compliance rate differs by a factor of 22 to 25, while the non-refusal rate differs by 18 to 23 percentage points. Without the three collapsing graders, compliance differs by a factor of 1.7 to 2.8 on all four corpora, and non-refusal still differs by 10 to 22 points. These results do not change when every grader is called once. We propose a reporting minimum that keeps the compliance rate and adds its per-class label counts and the named grader. Beside it goes the non-refusal rate, whose disagreement across graders is additive.

Explore related subjects

cs.CRcs.CLcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruqiya Bin Safi. 2026-10-17. The Missing Compliance Class: LLM Graders That Collapse Jailbreak Success, and a Free Check. https://arxiv.org/abs/2610.24872

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects

Vector databases serve as the retrieval backbone of modern AI applications, yet their security remains largely unexplored. We propose the Black-Hole Attack, a poisoning attack that injects a small number of malicious vectors near the geometric center of the stored vectors. These injected vectors attract queries like a black hole and frequently appear in the top-k retrieval results for most queries. This attack is enabled by a phenomenon we term centrality-driven hubness: in high-dimensional embedding spaces, vectors near the centroid become nearest neighbors of a disproportionately large number of other vectors, while this centroid region is nearly empty in practice. The attack shows that vectors in a vector database cannot be blindly trusted: geometric defects in high-dimensional embeddings make retrieval inherently vulnerable. Based on this insight, we propose four attack paths tailored to different attacker capabilities. Our experiments show that up to 94.4% of queries are successfully attacked. Additionally, we study two directions of defense: hubness mitigation and detection-based filtering. Hubness mitigation either significantly reduces retrieval accuracy or provides only limited protection, while the detection-based defense is effective against some attack paths but fails against others. A robust and adaptive defense thus remains an open problem, and our findings indicate that vector databases require more careful treatment of security.

cs.CR↗
arXiv2026

Anywhere, Any-Stymie: Remote Activation of Trojan Malware on LiDAR with Modulated Signals

LiDAR sensors are widely deployed in autonomous systems for safety-critical 3D perception. We identify a previously unexplored attack surface in which dormant malware, embedded in the LiDAR sensing pipeline, remains inactive during normal operation and is triggered externally after deployment, without access to sensor hardware or networking at attack time. This malware was developed in a closed research test environment with vendor technical support, rather than by exploiting an inherent production supply-chain vulnerability. To trigger the malware, we repurpose the sensor's own native dual-return signal processing as a covert channel for a modulated optical signal delivered into the environment. Once triggered, the malware performs false data injection and suppression on static and mobile platforms. Unlike optical spoofing, where the transmitted signal is itself the attack, here it carries only a small key that the sensor decodes with existing signal processing. We establish feasibility with static triggering at 300~ft and drive-by runs reaching 35 mph, and verify that injected artifacts are taken up as detections by a fine-tuned 3D object detector. On a deployed tactical autonomous vehicle running an unmodified production autonomy stack, we demonstrate both fail-safe and fail-dangerous impact: emergency stop, mission abort, and collision. These results motivate stronger integrity guarantees across LiDAR development and deployment pipelines.

cs.CR↗
arXiv2026

VeriRouter: Towards Verifiable LLM Routing

Large language model (LLM) routers are increasingly deployed to optimize quality, latency, and cost through dynamic model selection and request rewriting. However, a compromised router can cause ineligible selection on the control plane, unendorsed rewriting or unfaithful relay on the data plane, undermining LLM routing integrity. We present VeriRouter, a verifiable LLM routing framework. Under an endorsed public context binding a private semantic index and rewrite banks, VeriRouter combines ZK proofs with a provider-issued serving certificate to establish integrity across both planes while keeping the router's private routing assets hidden from clients. For model selection, we design an efficient ZK proof for semantic search. For rewriting, local checks enforce message preservation and additions from committed banks. On real-world workloads, VeriRouter's model-selection and rewrite proofs average 21.92~s and 4.24~s, yielding 1,307$\times$ and 101$\times$ speedups over the fastest completing baselines. Relative to serving without verification, VeriRouter adds only 4.08 s under per-turn selection and 0.11 s under session-pinned selection.

cs.CR↗