Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks
- URL: http://arxiv.org/abs/2607.28685v1
- Date: Thu, 30 Jul 2026 03:45:10 GMT
- Title: Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks
- Abstract summary: Three agent-safety benchmarks rank the same 18 models differently.<n>R-Judge specificity against AgentHarm safety correlates $-0.64$ at $n=7$ and $+0.02$ at $n=18$, and a quarter of random size-7 subsets reach $|| geq 0.5$ around that near-zero value.<n>AgentHarm shows the strongest held-out association, $=+0.72$ with three-template jailbreak safety after controlling capability.
- Score: 10.839956062772792
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, InjecAgent, AgentHarm, AgentDojo) as measurements to be validated, running each under its official implementation and author-provided scorer on up to 22 models, with MMLU and GPQA measured by us under one protocol as a capability composite. The metric is the first problem. On any binary trace-judgment benchmark scored by $F_1$, an ``always positive'' policy attains $F_1 = 2π/(1+π)$; on R-Judge that is $0.690$, above five of the 21 models that actually discriminate. The three broad-coverage benchmarks then rank the same 18 models differently, and the trade-off behind that disagreement is a small-panel artifact: R-Judge specificity against AgentHarm safety correlates $-0.64$ at $n{=}7$ and $+0.02$ at $n{=}18$, and a quarter of random size-7 subsets reach $|ρ| \geq 0.5$ around that near-zero value. Held-out validity turns on which outcome you pick. Capability predicts task success ($ρ{=}{+}0.60$) but correlates negatively with misalignment safety ($ρ{=}{-}0.44$, $n{=}21$). On their paired $n{=}20$ panel, the corresponding contrast is $Δ{=}{-}1.00$ (95% CI $[-1.48, -0.49]$, $p<0.001$), and it survives leave-one-organization-out and organization-clustered bootstrap analyses. On an expanded 41-model panel, the misalignment correlation weakens to $-0.16$ (95% CI $[-0.54, +0.22]$) and jailbreak strengthens to $+0.34$, though neither change is significant. \mbox{AgentHarm} shows the strongest held-out association, $ρ{=}{+}0.72$ with three-template jailbreak safety after controlling capability. But both instruments score harmful compliance, so this is evidence of convergent validity rather than general safety. Naming the benchmark, metric, target behavior, and model panel is the minimum a safety claim needs.
Related papers
- Ockhamareto: Pareto-Gated Segment-Level Credit Assignment for Concise Unit-Test Generation with Reinforcement Learning [64.49364696628294]
We introduce textbfOckhamareto, a single-shot GRPO framework for unit-test generation and selection.<n>Ockhamareto has two principal components: (i)a emphPareto-gated Bonus that rewards only rollouts non-dominated in(mutation, $-$#tests) space, and (ii)emphToken-level Segment Credit, which attributes each test's marginal mutation kills back to the tokens of its unit-test block.
arXiv Detail & Related papers (2026-08-25T12:20:02Z) - Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument, with Discrimination Bounded In Sample [0.0]
We build an instrument that reads from a single fit and with no oracle.<n>We report where the instrument is blind, a pre-registered negative where a neural estimator loses to Tikhonov-regularized inversion at recovery.
arXiv Detail & Related papers (2026-08-04T21:20:44Z) - Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment [0.7249731529275342]
Fidelity metrics, such as per-token KL divergence (KLD) against a high-precision reference, are often used in practice as low-cost proxies for benchmark quality.<n>We find that KLD is strongly correlated with benchmark score over the full cohort.<n>However, this relationship collapses to non-significance in the near-baseline silent zone.
arXiv Detail & Related papers (2026-06-17T19:59:26Z) - Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing [51.56484100374058]
Production LLM applications stack several defense families -- refusal-phrase filters, tokenbudget controls, model allowlists, rate limits, tool-registry authentication -- yetBAS benchmarks report a single aggregate coverage number.<n>We add four-LLM-Top-10-aware agents to a 21-agent baseline scanner and target a lattice of four synthetic LLM endpoints.
arXiv Detail & Related papers (2026-06-01T19:39:25Z) - Hidden-State Privacy Has an Empty Middle [51.56484100374058]
We prove a complementary Fisher-ball lower bound: every full-rank Gaussian release at $O(1)$ Fisher utility admits a direction whose Mahalanobis signal grows linearly in hidden width.<n>A split-memory transformer trained from scratch reaches $G_mathrmMah in [20, 33]$ at 90M and maintains a $6$--$24times$ advantage over same-budget GPT baselines from 30M to 1B at a fixed-token language loss penalty.
arXiv Detail & Related papers (2026-05-21T20:12:09Z) - MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents [0.0]
We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework.<n>We show faithful evaluation increases measured attack success by $4times$ (ASR-R: 0.25 to 1.00$)<n>Our primary contribution is MEMSAD, a calibration-based defense grounded in a gradient coupling.
arXiv Detail & Related papers (2026-05-05T08:15:41Z) - When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors [66.18091962164219]
Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model's autonomous preferences.<n>We propose two complementary metrics to isolate non-mandatory behavioral patterns: textbfResponse Pattern Similarity (RPS) for verbal alignment and textbfAction Graph Similarity (AGS) for tool-use habits modeled as directed graphs.
arXiv Detail & Related papers (2026-04-23T03:48:56Z) - Hardware Validation of DAGI via a Modular "Ridge" Signature and High-Order Synergistic Information [0.0]
DAGI (Directed Acyclic Graph Information) framework on IBM Quantum hardware.<n>Small, controlled experiment whose ideal output distribution is constrained to a low-dimensional modular manifold (a "ridge)<n>Key recovery exceeds chance: per-shot accuracy 0.1689 (chance 0.125, 95% Wilson CI [0.1610, 0.1772])<n>These results support the claim that DAGI detects and quantifies non-trivial, hardware-resilient information structure.
arXiv Detail & Related papers (2026-04-16T14:16:59Z) - Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents [0.9368753183086048]
We introduce **AgentFence**, an architecture-centric security evaluation that defines 14 trust-boundary attack classes spanning planning, memory, retrieval, tool use, and delegation.<n>Hold the base model fixed, we evaluate eight agent archetypes under persistent multi-turn interaction.<n>Highest-risk classes are operational: Denial-of-Wallet ($0.62 pm 0.08$), Authorization Confusion ($0.54 pm 0.10$), Retrieval Poisoning ($0.47 pm 0.09$), and Planning Manipulation ($0.44 pm 0.11$)
arXiv Detail & Related papers (2026-02-07T18:27:47Z) - Near Sample-Optimal Reduction-based Policy Learning for Average Reward
MDP [58.13930707612128]
This work considers the sample complexity of obtaining an $varepsilon$-optimal policy in an average reward Markov Decision Process (AMDP)
We prove an upper bound of $widetilde O(H varepsilon-3 ln frac1delta)$ samples per state-action pair, where $H := sp(h*)$ is the span of bias of any optimal policy, $varepsilon$ is the accuracy and $delta$ is the failure probability.
arXiv Detail & Related papers (2022-12-01T15:57:58Z) - Toward Adversarial Robustness via Semi-supervised Robust Training [93.36310070269643]
Adrial examples have been shown to be the severe threat to deep neural networks (DNNs)
We propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_stand$ and $R_rob$)
arXiv Detail & Related papers (2020-03-16T02:14:08Z) - Curse of Dimensionality on Randomized Smoothing for Certifiable
Robustness [151.67113334248464]
We show that extending the smoothing technique to defend against other attack models can be challenging.
We present experimental results on CIFAR to validate our theory.
arXiv Detail & Related papers (2020-02-08T22:02:14Z)
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.