Step 10: tool results

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{"ok":true,"arxiv_id":"2605.09684","file":"MonitoringBench.tex","kind":"text","text":"nd mode-collapse: Red-teaming pipelines produce more diverse attacks}\n\n\\subsubsection{Taxonomy-guided red-teaming reduces mode collapse}\n\nDirect elicitation collapses to a narrow region of the taxonomy (Fig.~\\ref{fig:results-diversity}). Opus-only attacks cluster in T3 (59\\%) and 64\\% share the same structural template in S3 (multi-file but tightly clustered). In practice, these attacks reuse a small set of mechanisms alongside superficial camouflage. For example, firewall attacks repeatedly whitelist an IP range and differ only in whether the change is framed as testing, debugging, or other benign-sounding actions. In contrast, our strategy generation pipeline explores the attack surface more across the taxonomy, and covers both simple and complex mechanisms.\n\nThis broader coverage surfaces monitor blind spots that do not arise under direct elicitation. We analyze the taxonomic distribution of uncaught attacks (Appendix~\\ref{app:mode-collapse}) and show that the taxonomy allows us to discover more diverse uncaught attack types. We find that averaged across seven monitors, 40\\% of uncaught attacks fall into T1, despite T1 comprising only 2\\% of the Opus-only baseline. These results highlight two points: (1) evasion is not confined to complex mechanisms and even simple T1 attacks can bypass monitors when appropriately framed and (2) broader red-teaming can reveal failure modes that would otherwise remain undetected, making it necessary for reliable monitor evaluation.\n\n\n% In Figure \\ref{fig:results-diversity}, we demonstrate mode collapse in attack generation, which we reduce with a\n% red-teaming taxonomy. The goal of strategy generation was to explore the attack surface by covering taxonomy categories and its combinations. We then label Opus-only, strategy generated and post-refinement trajectories with Opus 4.5 as an LLM judge and compare their distributions across the taxonomy (Appendix \\ref{app:attack_labels}). Figure \\ref{fig:results-diversity} shows that Opus-only elicitation is concentrated in a narrow region of the taxonomy, showing mode collapse. Technically, 59\\% of attacks fall into T3, and structurally, 64\\% into S3 (multi-file but tightly clustered edits). In practice, Opus-only attacks often reuse the same small set of core strategies with only superficial camouflage, e.g. in the firewall side task, the underlying mechanism is often to whitelist a new IP range and differ the framing as testing, debugging, or other benign-sounding maintenance change. In contrast, our strategy generation pipelines explore the attack surface more with a wider distributions across all taxonomy axes, which helps to discover novel attacks. Furthermore, in Appendix \\ref{app:mode-collapse} we present uncaught attack distributions and show that the taxonomy allows us to discover more diverse uncaught attack types. We find that 40.17\\% of uncaught attacks (averaged across 7 monitors) fall into T1, despite T1 comprising only 2\\% of the Opus-only baseline. This is crucial as defending against a wider distribution of attacks provides better safety guarantees.\n\n  %This matters because high monitor catch rates on model-elicited attacks may reflect robustness to a narrow set of repeated patterns, rather than the broader space of possible implementations. We further compare uncaught attack distributions between Opus-4.5 and MonitoringBench in Appendix \\ref{app:mode-collapse}.\n\n% Figure \\ref{fig:results-diversity} illustrates the mode-collapse claim and shows that our attack generation pipelines produce attacks across the full red-teaming taxonomy.\n\n% Direct elicitation tends to produce a localized view of the attack surface. In practice, Opus converges on a small set of workable patterns and repeats nearby variants. While these attacks may succeed, they primarily test monitor performance on a narrow family of implementations. Our construction pipeline reduces this concentration, enabling a more informative evaluation of monitor robustness. \n% \\textcolor{red}{repetitive to taxonomy} The result is not diversity for its own sake, but \\textbf{coverage as a precondition for meaningful monitor evaluation}. Strong performance on a dense cluster of attacks does not necessarily indicate robustness across the broader space of ways the same side task can be achieved. \\sout{a monitor that performs well on one dense cluster of attacks has not shown robustness to the broader space of ways the same side task can be achieved.}\n\n\\subsubsection{Conceive-execute gap: Decomposed red-teaming produces stronger attacks}\n \nOur main result in Figure \\ref{fig:pipeline-and-results} shows that \\textbf{refinement consistently makes poorly executed attacks better}, achieving the lowest catch rates across all monitors (dev and held-out), monitor types (FT and SW), and attack sources (Opus-only, Human-in-the-loop, Recon, and Recon+Tools); see Appendix \\ref{app:pipe-results} for full details. The biggest gains up to 40\\% appear on the strongest full trajectory monitors between Opus-only and Opus-only refined attacks, for example, GPT-5.1 Codex score goes from 69.9\\% to 49.8\\% post-refinement and GPT-5 performance reduces even further from 60.2\\% to 19.5\\%. Given that refinement's main goal is cleaning up suspicious code, redundant validation, and unnecessary reconnaissance, this suggests that the biggest red-teaming bottleneck is poor execution. The improvements from separating strategy generation, execution, and refinement provide evidence for a \\textbf{conceive-execute gap}: models can conceive and execute strong attacks, but struggle to do both in the same trajectory.\n\nStrategy generation produces not only diverse but also stronger attacks, as both types of recon agents outperform the Opus-only baseline on strong to mid-tier monitors, despite being optimized primarily for exploration rather than stealth. For example, Opus-only has a catch rate of 94.9\\%, while Recon and Recon+Tools score 82.7\\% and 73.5\\%, respectively, on Opus-4.5 monitor. Similarly on GPT-5.1 Codex, catch rates go from 70\\% to 67\\% and 54.5\\% between Opus-only and the two recon agents. \n\n% However, recon strategies optimize for diversity and exploration, which sometimes trades off stealth against weaker monitors: the broader coverage finds stronger attacks as well as weaker ones, with Opus-only scoring lower catch rates than recon strategies on GPT-4.1, GPT-5-Nano and GPT-4.1-Mini. agree girl <3 i think this is my last ~nit~ im happy with everything else <3 \n\nThis pattern is less consistent on weaker monitors. Human and Recon strategies have higher catch rates than Opus-only on GPT-4.1, GPT-5-Nano and GPT-4.1-Mini. Since these strategy generation stages are designed to overcome mode collapse by exploring a wider range of feasible attacks, they sometimes introduce more explicit or convoluted strategies that some weaker monitors catch more easily. \n\nAcross the non-refined pipelines, catch rates already fall as strategy generation improves. Refinement extends this further, with each refined pipeline outperforming its non-refined counterpart. We show that refinement is most effective when applied to stronger starting strategies such as Recon and Recon+Tools. This matters because both reconnaissance-based generation and refinement are automatable and can therefore scale with future model improvements. Full red-teaming details and discussion on pipeline generalization are given in Appendix~\\ref{app:pipes-dev}.\n\n% Recon and refinement agents providing the best overall results, in particular when applied together, is particularly promising as both pipelines are automated and designed to scale as model capabilities advance. Furthermore, the results show Human-in-the-loop attacks perform similar or lower to Opus-only baseline pre-refinement, but refined human strategy attacks outperform Opus-only refined attack. We also carried out smaller experiments to discuss promising results in pipeline generalization. We provide all the red-teaming details and results in Appendix \\ref{app:pipes-dev}.\n\nTogether, pipeline stages contribute complementary benefits: human strategies expand coverage, reconnaissance grounds strategies in the environment, monitor-aware tools optimize for stealth, and refinement improves overall execution quality. They produce a broader and more challenging attack distribution than direct elicitation alone, addressing the identified red-teaming challenges.\n\n %Figure \\ref{fig:pipeline-and-results} compares attack generation methods with a baseline of an elicited-only model, showing that Opus-alone attacks are easiest to catch for most monitors with Opus-only being caught at 94.8\\%, with refinement reducing the catch rate to 88.4\\%. \n\n%Refinement of Recon and Recon+Tools agents shows the best overall results, followed by refined Opus+Human strategies. This is particularly promising, as both stages are automated and designed to scale as model capabilities advance. The fact that recon (separating strategy generation) and refinement (separating execution) each independently improve attacks -- and that their combination is strongest -- further supports the conceive-execute interpretation.\n\n% Figure 1 compares attack generation methods against a baseline of an elicited-only model, showing that Opus-alone attacks are easiest to catch for most monitors and that separating strategy generation from execution via refinement significantly outperforms the baseline – Opus-only is caught at 94.8%, while Opus+refinement is caught at 88.4%. This is direct evidence of the conceive-execute gap: models can craft strong attacks and they can evade monitors, but they cannot reliably do both within a single trajectory.229\n\n% In control evaluations, red-team is tasked with doing the main task, side task, and evading monitors -- all within a single trajectory. This is difficult for current frontier models, and we show that decomposing red-teaming into sub-tasks can create stronger attacks. We call this a \\textbf{conceive-execute} gap. Figure \\ref{fig:pipeline-and-results} compares attack generation methods against a baseline of an elicited-only model, showing that Opus-alone attacks are easiest-to-catch for most monitors and separating strategy generation and refinement significantly outperforms it. This both is the evidence of the conceive-execute gap: models can craft strong attacks and evade monitors, but not both within single trajectory.\n\n% Our main result in Figure \\ref{fig:pipeline-and-results} shows that refinement consistently makes poorly executed attacks better as demonstrated by has its lowest catch rates across all monitors (dev and held-out), monitor types (FT and SW) and attack sources (Opus-only, Human-in-the-loop, Recon and Recon+Tools); see Appendix \\ref{app:pipe-results} for full details. The biggest gains of up to 30\\% seen on the best monitors, including Opus-4.5 taht drops score by \\textcolor{red}{X}\\%. Given the main refinement goal is cleaning up suspicious code, redundant validation, unnecessary reconnaissance, suggests that the biggest red-teaming bottleneck is poor execution.\n\n% Refinement of Recon and Recon+Tools agents shows the best overall results, followed by refined Opus+Human strategies. This is particularly promising, as both stages are automated and designed to scale as model capabilities advance. Furthermore, Recon strategies with refinement providing the best attacks is further evidence for conceive-execute gap: models can produce strong attacks \\textit{and} evade monitors, but not both within single trajectory.\n\n% In addition, the main results show that strategy generation, which primarily focused on exploration and attack diversity, produces not only diverse but also stronger attacks, as the both types of recon agents outperform Opus-only baseline on strong to mid-tier monitors. However, we see that strategy generation optimized for exploration can also produce weaker attacks as well, given the performance increase on weaker models. \n\n% Together, the pipeline stages contribute complementary benefits: human seeding expands coverage, reconnaissance grounds strategies in the environment, monitor-aware tools optimize for stealth, and refinement improves overall execution quality. Together, they produce a broader and more challenging attack distribution than direct elicitation alone and addresses the identified red-teaming challenges.\n%This suggests that the strategies generated by the recon agents can outperform both human iteration and Opus-only strategies alone. This is particularly promising given both stages are automated and designed to scale as model capabilities advance.\n\n%Our main result shows that refinement consistently produces the strongest attacks, significantly outperforms baseline Opus. In Appendix \\ref{app:pipe-results}, we further show that refinement performance transfers to all 10 held-out monitors FT and 7 SW monitors, with biggest gains of up to 30\\% seen on the best scoring monitors. Given the main goal refinement is cleaning up suspicious code, redundant validation, unnecessary reconnaissance, this suggests that the current red-teaming bottleneck is poor execution proving the conceive-execute gap we observe. \n\n%Furthermore, we see that refinement improves both Opus-only and human as well as recon agent generated trajectories, with both types of refinement offering significant red-team improvement, with recon refined strategies performing best overall. This suggests that the strategies generated by the recon agents can outperform both human iteration and Opus-only strategies alone. This is particularly promising given both stages are automated and designed to scale as model capabilities advance.\n\n% We observe a shift in behavior across later pipeline stages, though not as a strict monotonic ranking. Human-seeded attacks are sometimes easier to catch than Opus-only attacks. Since the goal is to overcome mode collapse, human seeding prioritizes diversity and successful execution. This therefore produces more explicit or convoluted attacks that are easier for monitors to detect. Their value lies in expanding the search space beyond the narrow patterns produced by direct elicitation.\n\n% Following manual human seeding, we find that the automated reconnaissance agent produces stronger attacks. By combining environment access with monitor feedback, the agent can iteratively probe which actions are flagged as suspicious and refine its strategy accordingly, leading to lower-suspicion implementations particularly against mid-tier monitors. This process benefits from additional steps and iterative reasoning, which enable more effective exploration of less detectable strategies.\n\n% Refinement produces the most consistent increase in difficulty. Refined attacks have the lowest catch rates across most monitors, suggesting that many attacks are caught due to careless execution signals. Refinement cleans up suspicious comments, redundant validation, unnecessary reconnaissance, and standalone code paths whose primary purpose is to support the side task. We also find it useful to guide refinement with a task-conditioned decision tree, which first asks whether the side task can be implemented through actions already licensed by the main task, and only otherwise falls back to more visible camouflage strategies. See more details in \\hyperref[app:refinement_pipeline]{Appendix~\\ref*{app:refinement_pipeline}}.\n\n\n\n\\subsection{MonitoringBench: evaluating current monitoring capabilities}\n\\begin{figure}[t]\n    \\centering\n\n        \\includegraphics[width=\\linewidth]{figures/monitorbench_sw_ft_2644.png}\n    \\caption{Final MonitoringBench results: Full Trajectory (1\\% FPR) and Stepwise (1\\% per-action FPR) Catch rates. Lower catch rate indicates stronger attacks.}\n    \\label{fig:monitoringbench}\n\\end{figure}\n\\label{sec:mbench_evals}\n\n\n\n%We evaluate monitors on the final MonitoringBench attack suite, constructed from across all the generated attack pipelines to balance three criteria: difficulty graded to allow discriminating across a range of monitor capability, hardest attacks for future monitors and balanced to represent our attack taxonomy. We report results on two monitor types: full trajectory and stepwise (or action-based). \n%We evaluate monitors on the final MonitoringBench attack suite, constructed by sampling across our attack pipelines to satisfy three criteria: (1) difficulty grading that of across monitor capabilities, (2) inclusion of the strongest attacks to remain challenging for future monitors, and (3) coverage across our attack taxonomy. We report results on two monitor types: full trajectory and stepwise (or action-based). Because we use a shared monitor prompt, these results reflect broad monitoring capability comparisons rather than per-model optimized monitor performance.\n\nWe present MonitoringBench results for two monitor types: full-trajectory and stepwise (Figure~\\ref{fig:monitoringbench}). Monitoring capability is concentrated in frontier models, with significant performance drops for weaker models across both settings. Results here reflect broad monitor capability trends, as models were not individually elicited.\n\nIn the full-trajectory setting, Opus~4.5 achieves the highest catch rate at roughly 70\\%. A mid-tier group (Sonnet~4.5, GPT-5.1 Codex, Sonnet~4.0, GPT-5) follows, while smaller models drop off steeply. This capability gradient suggests that asynchronous monitoring of strong sabotage trajectories may require frontier-level models. In practice, weaker models are preferred as monitors due to lower cost and the assumption that they are less likely to behave deceptively \\citep{greenblatt2024ai}, yet our results show they are less reliable against stronger attacks.\n\nStepwise monitoring shows the same general ordering but higher absolute catch rates—Opus~4.5 catches roughly 83\\% of attacks, with mid-tier models clustering lower at 25–52\\%. However, these numbers are not directly comparable to full-trajectory results. Since stepwise monitors score each action independently, a 1\\% per-action FPR compounds to an effective trajectory-level FPR of 6.7–17.4\\% across monitors (Appendix~\\ref{app:sw-effective-fpr}). The higher catch rates reflect both the benefits of stepwise monitoring such as additional context from tool call outputs and shorter contexts as well as a more permissive effective threshold.\n\nOverall, while frontier monitors already show promising results, stronger attacks produced via decomposed red-teaming reveal crucial failure modes, which we analyze in Section~\\ref{sec:failures}. These results are a snapshot of current capabilities: as monitors improve, static attack suites will saturate, and future monitor evaluations should be red-teamed with attacks generated from the strongest available models and red-teaming pipelines. \n\n\n\n%The results suggest two main trends. First, better models are better monitors: this is early evidence that general capability is tied to monitoring capability. \n\n%Second, models perform better as stepwise monitors than in the full trajectory monitors, suggesting that access to intermediate tool outputs and shorter decision contexts improves detection.\n%The best monitor catches a large fraction of attacks, but most monitors miss the majority, and even Opus 4.5 missing roughly 450 attacks in stepwise. \n%These results measure conditional detection of successful curated attacks, not the natural rate at which an attacker model would discover them.\n%[Mention that we tested monitor prompt variations and no prompt shows consistent improvement across ALL monitors, at different FPR levels etc “\\textit{We find that improvements were not robust and prompt changes that help one monitor model can hurt another, and results vary substantially across models and runs”}]\n\n\n\\subsubsection{Monitoring failures}\n\\label{sec:failures}\n\\begin{figure}[t]\n    \\centering\n\n    \\includegraphics[width=\\linewidth]{figures/appendix/failure_taxonomy_stacked_new.","chars":20000,"total_chars":97470,"offset":60000,"truncated":true,"next_offset":80000}

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{"ok":true,"url":"https://arxiv.org/abs/2508.19461","text":"[2508.19461] Reliable Weak-to-Strong Monitoring of LLM Agents\nSkip to main content\nSearch\nSubmit\nDonate\nLog in\nSearch arXiv\nPress Enter to search · Advanced search\n-->\nComputer Science > Artificial Intelligence\narXiv:2508.19461 (cs)\n[Submitted on 26 Aug 2025]\nTitle: Reliable Weak-to-Strong Monitoring of LLM Agents\nAuthors: Neil Kale , Chen Bo Calvin Zhang , Kevin Zhu , Ankit Aich , Paula Rodriguez , Scale Red Team , Christina Q. Knight , Zifan Wang\nView a PDF of the paper titled Reliable Weak-to-Strong Monitoring of LLM Agents, by Neil Kale and 7 other authors\nView PDF\nHTML (experimental)\nAbstract: We stress test monitoring systems for detecting covert misbehavior in autonomous LLM agents (e.g., secretly sharing private information). To this end, we systematize a monitor red teaming (MRT) workflow that incorporates: (1) varying levels of agent and monitor situational awareness; (2) distinct adversarial strategies to evade the monitor, such as prompt injection; and (3) two datasets and environments -- SHADE-Arena for tool-calling agents and our new CUA-SHADE-Arena, which extends TheAgentCompany, for computer-use agents. We run MRT on existing LLM monitor scaffoldings, which orchestrate LLMs and parse agent trajectories, alongside a new hybrid hierarchical-sequential scaffolding proposed in this work. Our empirical results yield three key findings. First, agent awareness dominates monitor awareness: an agent's knowledge that it is being monitored substantially degrades the monitor's reliability. On the contrary, providing the monitor with more information about the agent is less helpful than expected. Second, monitor scaffolding matters more than monitor awareness: the hybrid scaffolding consistently outperforms baseline monitor scaffolding, and can enable weaker models to reliably monitor stronger agents -- a weak-to-strong scaling effect. Third, in a human-in-the-loop setting where humans discuss with the LLM monitor to get an updated judgment for the agent's behavior, targeted human oversight is most effective; escalating only pre-flagged cases to human reviewers improved the TPR by approximately 15% at FPR = 0.01. Our work establishes a standard workflow for MRT, highlighting the lack of adversarial robustness for LLMs and humans when monitoring and detecting agent misbehavior. We release code, data, and logs to spur further research.\nComments:\n18 pages, 15 figures\nSubjects:\nArtificial Intelligence (cs.AI) ; Cryptography and Security (cs.CR); Machine Learning (cs.LG)\nCite as:\narXiv:2508.19461 [cs.AI]\n(or\narXiv:2508.19461v1 [cs.AI] for this version)\nhttps://doi.org/10.48550/arXiv.2508.19461\nFocus to learn more\narXiv-issued DOI via DataCite\nSubmission history\nFrom: Neil Kale [ view email ] [v1]\nTue, 26 Aug 2025 22:29:31 UTC (5,253 KB)\nFull-tex","chars":2800,"total_chars":5258,"offset":0,"truncated":true,"next_offset":2800}

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