{"id":"f84607f1-4dd9-48ea-8580-dd40c90b7ab1","arxiv_id":"2606.10484","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"AgentCanary introduces an Entry × Impact risk taxonomy, high-fidelity real tool environments with persistent state, and multi-dimensional trajectory evaluation to assess AI agent security across models and attacks.","lead":"The paper introduces AgentCanary, a framework with a new risk taxonomy, real executable environments, and trajectory-based scoring to test security of autonomous AI agents. Smart generalists should read it because AI agents are moving from chat to executing real tasks on systems, creating new compromise risks that need systematic testing.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"The Entry × Impact taxonomy's instantiated task suite may not faithfully represent real agent workflows and attack surfaces.","rationale":"The reader's weakest assumption matches the load-bearing point exactly. The abstract and summary give no indication of independent fidelity checks on the taxonomy instantiation, so the concern stands as the primary risk to generalizing the failure observations. No stronger internal inconsistency or missing formal element appears from the given material.","tokens_in":1762,"tokens_out":330,"duration_ms":12491,"concrete_test":"Select 20 tasks from the suite; have 3 independent AI-agent security practitioners rate each on a 1-5 realism scale against their experience with production deployments (blind to paper claims); compute mean score and inter-rater agreement. If mean < 3.5 or agreement < 0.6, rerun the main experiments on a revised task subset filtered to high-realism items and check whether the headline failure rates change by >15%.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim—that agents fail to recognize attacks especially under compromised skills, persistent state, and long-horizon execution—depends on the scenario-aligned tasks being realistic proxies. The paper defines an orthogonal taxonomy and states it is instantiated into a task suite spanning deployment workflows, but provides no external validation (e.g., expert review, mapping to documented incidents, or comparison against production agent logs). If the chosen tasks over- or under-emphasize certain entry/impact combinations relative to actual usage, the observed failure patterns could be artifacts of the constructed environment rather than general properties of frontier agents.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces AgentCanary, a security evaluation framework for autonomous AI agents operating in executable environments. It contributes (1) an orthogonal Entry × Impact risk taxonomy instantiated as a scenario-aligned task suite covering realistic deployment workflows, (2) a high-fidelity real executable environment supporting persistent state and long-horizon interactions instead of static or mocked setups, and (3) trajectory-grounded multi-dimensional scoring along Outcome Safety, Security Awareness, and Task Utility. Evaluation of frontier models across three agent frameworks and multiple adversarial attacks shows that agents frequently fail to recognize attacks, especially under compromised skills, persistent state, and long-horizon execution.","tokens_in":1871,"tokens_out":457,"duration_ms":17148,"significance":"If the task suite is shown to be representative, the framework would meaningfully advance agent security evaluation by addressing fragmented coverage, low-fidelity environments, and coarse metrics in prior work. The real executable environment with persistent state and the decomposed trajectory-based metrics are concrete strengths that enable more realistic long-horizon attack testing than existing benchmarks.","major_comments":[{"comment":"The description of the Entry × Impact taxonomy and its instantiation into the task suite states that the suite spans realistic deployment workflows and attack surfaces, yet provides no external validation (expert review, mapping to documented incidents, or comparison to production agent logs). This assumption is load-bearing for the central empirical claim that observed failure patterns reflect general properties of frontier agents rather than artifacts of the constructed tasks.","section":"comprehensive risk coverage contribution and task suite description"},{"comment":"The evaluation section reports that agents 'often fail to recognize the attacks they face' but supplies no quantitative metrics, per-model or per-attack breakdowns, error analysis, or statistical measures. Without these, the magnitude, consistency, and reliability of the failure patterns cannot be assessed.","section":"evaluation results and abstract"}],"minor_comments":[{"comment":"The abstract refers to evaluation 'against multiple established adversarial attack methods' but does not enumerate them; adding an explicit list or table would improve reproducibility.","section":"abstract and evaluation setup"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"Thank you for the constructive feedback on AgentCanary. We address each major comment below, indicating where revisions will be made to improve clarity, transparency, and support for the claims.","responses":[{"response":"We agree that stronger external grounding would better support generalizability claims. The taxonomy and tasks were derived from a synthesis of agent security literature, common workflows in frameworks such as LangChain and AutoGPT, and known attack surfaces (e.g., tool misuse, prompt injection). In revision we will add an appendix with explicit mappings of each task to documented real-world agent incidents and use cases drawn from public reports, plus a limitations subsection acknowledging the absence of formal expert review or production-log validation. This increases transparency on task construction while remaining honest about the scope of validation performed.","revision_made":"partial","referee_comment":"[comprehensive risk coverage contribution and task suite description] The description of the Entry × Impact taxonomy and its instantiation into the task suite states that the suite spans realistic deployment workflows and attack surfaces, yet provides no external validation (expert review, mapping to documented incidents, or comparison to production agent logs). This assumption is load-bearing for the central empirical claim that observed failure patterns reflect general properties of frontier agents rather than artifacts of the constructed tasks."},{"response":"The manuscript currently emphasizes qualitative patterns in the abstract and high-level summary. The detailed evaluation (Section 4) contains per-model and per-attack results in tables and figures, including decomposed scores for Outcome Safety, Security Awareness, and Task Utility across the tested models, frameworks, and attacks. To address the concern, we will revise the abstract and add a concise results summary table with key quantitative metrics (failure rates, breakdowns) plus basic statistical descriptors in the main text. This makes the magnitude and consistency of observed patterns directly assessable without altering the core findings.","revision_made":"yes","referee_comment":"[evaluation results and abstract] The evaluation section reports that agents 'often fail to recognize the attacks they face' but supplies no quantitative metrics, per-model or per-attack breakdowns, error analysis, or statistical measures. Without these, the magnitude, consistency, and reliability of the failure patterns cannot be assessed."}],"tokens_in":1461,"tokens_out":444,"duration_ms":17252,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's useful move is building an evaluation setup that lets agents run against real tools with persistent state instead of mocked responses or static questions. That lets them test long-horizon attacks and compromised skills in a way earlier work mostly skipped. The orthogonal taxonomy that splits entry from impact, plus the three-axis scoring on full trajectories, gives a cleaner way to break down what goes wrong than single-score approaches.\n\nThey apply it to frontier models across a few agent frameworks and report that agents often miss the attacks, especially when state persists or the horizon is long. That baseline is worth having.\n\nThe soft spot is the task suite itself. The abstract says it is instantiated from the taxonomy and spans realistic workflows, but there is no mention of external checks such as expert review, mapping to documented incidents, or comparison with production logs. If the chosen entry-impact combinations do not line up with how agents are actually used, the failure patterns could be tied to the test construction rather than general agent behavior. The abstract also gives no numbers or error analysis, so the full paper needs to show the quantitative results and how the scoring was validated.\n\nThis is aimed at groups building or auditing agent systems who need a structured way to measure security. It is coherent enough on its own terms to go to referees, even if the realism question will need addressing in revision.","headline":"AgentCanary's real executable environment and Entry × Impact taxonomy are the concrete advances, but the task suite's match to actual workflows remains the main uncertainty.","tokens_in":2376,"tokens_out":349,"would_cite":false,"duration_ms":13903,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Current AI agents frequently fail to recognize attacks involving compromised skills, persistent state, and long-horizon execution.","keywords":["autonomous AI agents","security evaluation","adversarial attacks","executable environments","risk taxonomy","trajectory evaluation","persistent state","agent frameworks"],"falsifier":"Demonstrating that agents which fail on the AgentCanary tasks successfully detect and avoid the same attack types when placed in live user deployments outside the evaluated frameworks.","tokens_in":2664,"feed_emoji":"🛡️","tokens_out":643,"duration_ms":15728,"temperature":0.7,"pith_summary":"The paper presents AgentCanary as a framework that evaluates autonomous AI agents through a risk taxonomy, real executable environments, and full-trajectory scoring. It decouples attack entry points from resulting harms to build a task suite covering realistic workflows, then runs agents against actual tools with dynamic state changes. This setup exposes that frontier models miss many attacks when interactions persist across steps or when skills are altered. A sympathetic reader would care because earlier evaluations relied on static questions or mocked responses that cannot reveal these long-running threats.","feed_headline":"AI agents miss attacks in long-horizon real-tool executions","feed_subtitle":"AgentCanary runs frontier models against persistent-state threats and finds they overlook compromised skills and extended attack sequences.","key_machinery":"The Entry × Impact risk taxonomy that decouples adversarial entry from resulting harm, together with a real executable environment that maintains persistent state and supports long-horizon interactions.","core_discovery":"AgentCanary introduces an orthogonal Entry × Impact risk taxonomy that separates how adversarial influence enters the agent from the ultimate harm it causes, instantiates the taxonomy as a scenario-aligned task suite, executes agents in a high-fidelity environment with real tools and persistent state across multi-step interactions, and scores complete trajectories on the three dimensions of Outcome Safety, Security Awareness, and Task Utility, showing that current agents often fail to recognize the attacks they face, particularly under compromised skills, persistent state, and long-horizon execution attacks.","pith_inferences":["The multi-dimensional scoring could allow developers to train agents on security awareness as a distinct objective from task utility.","Extending the real executable environment to additional software ecosystems would test whether the observed failures generalize beyond the current three frameworks.","The taxonomy structure might serve as a template for evaluating security in other autonomous systems that execute multi-step tasks."],"forward_implications":["Security evaluations of agents must consume full trajectories rather than single replies or isolated tool calls.","Persistent state across multiple steps creates attack surfaces that static or mocked environments cannot capture.","Agents need separate mechanisms to maintain security awareness when skills are compromised or tasks extend over long horizons.","An orthogonal taxonomy of entry and impact provides broader risk coverage than earlier fragmented approaches."],"fun_headline_variants":["AI agents miss attacks in persistent real tool executions","AI agents fail at detecting long-horizon persistent threats","Agents overlook compromised skills in real executable tests","Frontier AI misses attacks during multi-step stateful runs"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The scenario-aligned task suite instantiated from the Entry × Impact taxonomy accurately represents realistic deployment workflows and attack surfaces in actual agent use.","fun_headline_variants_meta":{"raw":{"variants":["AI agents miss attacks in persistent real tool executions","AI agents fail at detecting long-horizon persistent threats","Agents overlook compromised skills in real executable tests","Frontier AI misses attacks during multi-step stateful runs"]},"model":"grok-4.3","cost_usd":0.006304,"raw_usage":{"total_tokens":3007,"prompt_tokens":756,"num_sources_used":0,"completion_tokens":59,"cost_in_usd_ticks":63037000,"prompt_tokens_details":{"text_tokens":756,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2192,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":756,"tokens_out":59,"duration_ms":14561,"temperature":1.0,"reasoning_tokens":2192,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T12:51:53.873411+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Demonstrating that agents which fail on the AgentCanary tasks successfully detect and avoid the same attack types when placed in live user deployments outside the evaluated frameworks.","supporting_citations":[],"review_version":1}