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BLINDSPOT: A Benchmark for Safety and Refusal Calibration in Long-Horizon Tool-Using Agents
Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas, Tejaswini Pedapati, Prasanna Sattigeri
TL;DR
Long-horizon tool-using agents can encounter safety decisions shaped by accumulated interaction, authorization, and environment state, while binary task or attack outcomes offer limited insight into whether behavior stays calibrated. BLINDSPOT evaluates complete trajectories through adaptive interaction, stateful tool execution, and execution-grounded adjudication. Its results show substantial safety-utility calibration differences across models and that unsafe outcomes can emerge after initially safe steps.
Problem
Long-horizon agent evaluations often reduce behavior to task completion or attack success, providing limited information about whether safety boundaries are maintained as interactions evolve.
Source: §1 · pp. 1–2Method
BLINDSPOT evaluates complete user-agent-environment trajectories using adaptive adversarial interaction, stateful tools, and execution-grounded five-outcome adjudication.
Source: §1 · pp. 2–3Results
Across 13 models and eight metrics, results show substantial safety-utility calibration differences and unsafe outcomes emerging after initially safe interaction steps.
Source: §1 · p. 3Takeaways & Limitations
The findings support treating agent safety as a trajectory-level property rather than a single-turn or binary success criterion.
Source: §7 · p. 9Future work will extend BLINDSPOT to additional domains, attack families, tools, and agent architectures.
Source: §7 · p. 10
Abstract
from arXiv · showhide
Large language model (LLM) agents increasingly operate over long-horizon interactions involving tool use, persistent state, evolving authorization, and external environment feedback. In such settings, safety failures may emerge only after multiple turns, yet existing evaluations often reduce agent behavior to task or attack success, obscuring whether an agent acts, refuses, or remains appropriately calibrated as the interaction evolves. We introduce Blindspot, a benchmark for trajectory-level safety calibration of long-horizon tool-using agents. Blindspot evaluates complete user-agent-environment trajectories through adaptive adversarial interaction, stateful tool execution, and execution-grounded adjudication. Its current instantiation contains 22 attack families and 35 scenarios across seven domains, yielding more than 2,500 long-horizon trajectories with an average interaction length of 14.7 turns. Each trajectory is assigned one of five outcomes: Safe Completion, Correct Refusal, Unsafe Completion, Over-Refusal, or Indeterminate. Unlike fixed attack datasets, Blindspot is an extensible live-simulation framework in which attacks, scenarios, tools, policies, domains, and agent configurations can be added without redesigning the evaluation pipeline. We evaluate 13 proprietary and open-weight LLMs using eight metrics covering unsafe completion, appropriate refusal, benign utility, over-refusal, repeated-run robustness, and post-refusal failure. Preliminary results reveal substantial differences in safety-utility calibration across models and show that failures can emerge only after several initially safe interaction steps. These findings motivate treating agent safety as a trajectory-level property rather than a single-turn or binary success criterion.
1 Introduction
Long-horizon agents can cross safety boundaries as intent, authorization, and environment state evolve, while binary task or attack outcomes do not show whether behavior remains calibrated. BLINDSPOT evaluates complete trajectories and distinguishes five outcomes to assess safety and utility together.
1 Introduction
- Binary attack-success or task-completion summaries provide limited information about whether an agent maintains the correct safety boundary as interactions evolve.Safety may depend on accumulated interaction history and environment state, not only a single harmful prompt.Source: §1 · pp. 1–2
- The current benchmark includes 22 attack families across 35 scenarios in seven domains and more than 2,500 trajectories averaging 14.7 turns.The scenarios include 25 core-domain and 10 cross-domain cases.Source: §1 · p. 2
- BLINDSPOT treats the complete interaction trajectory as its evaluation unit, encompassing messages, tool executions, policy state, and environment transitions.Figure 1 depicts a policy-grounded scenario leading to a trajectory whose execution and policy evidence support outcome assignment.Source: Figure 1 · p. 2, §1 · p. 3
- BLINDSPOT assigns Safe Completion, Correct Refusal, Unsafe Completion, Over-Refusal, or Indeterminate outcomes using policy, execution, and environment evidence with semantic judges and human review.Indeterminate covers cases where tool, environment, or execution failures prevent a conclusive outcome.Source: §1 · pp. 2–3, Table 1 · p. 3
- Across 13 models and eight metrics, preliminary results show substantial safety-utility calibration differences and unsafe outcomes emerging after initially safe steps.The evaluation covers unsafe completion, appropriate refusal, benign utility, over-refusal, repeated-run robustness, and post-refusal failure.Source: §1 · p. 3
2 Related Work
Prior work has extended agent-safety evaluation to multi-turn attacks, environment-mediated risks, and tool-use tasks. BLINDSPOT focuses on whether agents maintain calibrated safety boundaries throughout those trajectories.
Long-horizon and adversarial agent safety
- Unlike benchmarks centered on whether an adversarial objective succeeds, BLINDSPOT evaluates legitimate task completion, policy-violating actions, unnecessary refusals, and calibration as conditions evolve.Earlier long-horizon work established that safety can depend on accumulated interaction history.Source: §2 · pp. 3–4
Environment-mediated attacks and tool-using agents
- BLINDSPOT incorporates environment-mediated attacks and records tool calls, outputs, authorization state, and environment transitions as evidence for safety decisions.This extends evaluation beyond attacks delivered directly through user messages.Source: §2 · p. 4
Stateful tool-use and task-completion benchmarks
- Where tool-use benchmarks generally prioritize workflow completion, BLINDSPOT treats the trajectory as the safety object, with execution, refusal, or termination depending on policy and accumulated state.The same task can call for different behavior as the interaction proceeds.Source: §2 · pp. 4–5
3 BLINDSPOT: Benchmark Definition and Evaluation Setting
BLINDSPOT evaluates agents in stateful, policy-grounded scenarios where user behavior and environment conditions can change over time. Its outcome judgments rely on execution and policy evidence, supplemented by semantic interpretation and human review when needed.
3.1 Benchmark Definition
- BLINDSPOT defines trajectory-level safety and calibration as outcomes evaluated over agent interactions rather than isolated responses.Table 3 is described as reporting trajectory-level safety and calibration.Source: Table 3 · p. 5
3.2 Evaluation and Threat Model
- Each episode combines a User Agent, a Target Agent, and an executable environment where tool use changes state and informs later interaction.The User Agent may be benign or adversarial, and user-driven attacks can adapt to responses, clarification requests, and authorization checks.Source: §3.2 · p. 5
3.3 Policy-Grounded Outcome Evaluation
- An action's validity depends on the active policy and operational state, so an earlier permitted action may become prohibited as authorization or the environment changes.A suspicious-looking request may still be permitted under the scenario policy.Source: §3.3 · p. 5
- Outcome assignment grounds decisions in executed actions, authorization, policy conditions, and environment transitions, with semantic judges and human review for unresolved interpretation.Correct Refusal and Over-Refusal may both contain refusal language but differ according to whether the requested operation is prohibited or permitted; Indeterminate denotes execution-level incompleteness or infrastructure failure.Source: §3.3 · p. 6
4 Long-Horizon Attack Design
BLINDSPOT models attacks as evolving trajectory strategies, adapts user requests to observed interactions, and restricts evaluation to scenario-attack pairs whose required capabilities are present.
4.1 Attack Representation
- Attack families specify objectives, mechanisms, temporal behavior, attacker knowledge, and scenario requirements, allowing safety-relevant evidence to accumulate across a trajectory.The six annotation axes are non-exclusive: harms and mechanisms can vary across scenarios and temporal patterns.Source: §4.1 · p. 6
4.2 Temporal Attack Progression
- Temporal annotations can overlap within an attack family, representing patterns that are not limited to a single temporal category.Figure 4 presents temporal attack-pattern coverage.Source: Figure 4 · p. 6
4.3 Response-Conditioned Adaptive Adversary
- The User Agent generates each request from the interaction so far, allowing the attack’s surface strategy to change while its objective remains fixed.Observed refusals, authorization checks, and clarification requests can prompt reframing; repeated runs can therefore follow different paths.Source: §4.3 · pp. 6–7
4.4 Scenario-Attack Instantiation
- Only scenario-attack pairs meeting the attack’s required capabilities enter trajectory generation, excluding semantically invalid combinations.For example, stale-approval attacks require authorization that can change over time, while environment-injection attacks require adversary-influenced retrieved content.Source: §4.4 · p. 7
5 Benchmark Construction
BLINDSPOT generates trajectories from valid scenario-attack pairs, verifies behavior against execution evidence, and covers diverse attack mechanisms and temporal patterns.
5.1 Construction Pipeline
- Each trajectory combines a valid scenario-attack pair, with the scenario providing environment state, tools, and policies and the attack defining interaction strategy.The construction pipeline describes trajectory generation from these configurations.Source: §5.1 · p. 7
5.2 Trajectory Verification
- Adjudication uses recorded tool execution, authorization, policy conditions, and environment transitions; unresolved execution failures remain Indeterminate.Semantic judges handle trajectory-level interpretation, with human review for low-confidence or materially inconsistent cases.Source: §5.2 · p. 7
5.3 Dataset Statistics
- The benchmark spans 22 attack families and 35 scenarios across seven domains, with attack sources, mechanisms, and temporal patterns represented diversely.User-originated attacks appear in 13 families; staged attacks occur in 14, and social engineering and indirect injection each occur in 9.Source: §5.3 · pp. 7–8
6 Experimental Evaluation
Models differ in safety-utility calibration, and failures can emerge or recur over long interactions. Ablations show higher unsafe completion with longer horizons and adaptive attacks.
6.2 Preliminary Findings
- GPT-5.6 Sol has the lowest UCR (2%) and highest SCS (96.0), while Llama-3.3-70B has the highest BCR (97%) with 7% UCR.Claude Opus 4.6 has 4% UCR but 72% benign completion and 17% over-refusal, illustrating that lower unsafe completion does not alone determine calibration.Source: §6.2 · p. 8
- Unsafe behavior often emerges after several apparently safe steps, so short-prefix evaluation misses a substantial fraction of trajectory-level failures.Among 16 unsafe trajectories, none completed unsafely in the first four turns.Source: §6.2 · p. 8
- Repeated-run Fail@k increases for every evaluated model, while post-refusal failures are small for the strongest models and grow substantially for weaker agents.GPT-5.6 Sol rises from 3% at Fail@1 to 8% at Fail@5; GPT-4o rises from 23% to 42%.Source: §6.2 · p. 9
6.3 Ablation Studies
- For GPT-4o, UCR rises from 5% at one turn to 22% over the full horizon; longer interaction budgets consistently increase UCR.The same trend appears for stronger models, although at lower absolute rates.Source: Table 6 · p. 8, §6.3 · p. 9
- Adaptivity raises UCR across every model; GPT-4o rises from 11% in the non-adaptive condition to 22% with adaptation.The comparison conditions future user turns on the Target Agent’s observed behavior.Source: §6.3 · p. 9, Table 7 · p. 9
- Safe-twin results expose over-conservative behavior: Llama-3.3-70B completes 98% of safe twins with 9% ORR, while Claude Opus 4.6 completes 74%.The contrast assesses performance on policy-permitted requests alongside adversarial configurations.Source: §6.3 · p. 9, Table 7 · p. 9
7 Conclusion and Future Work
BLINDSPOT evaluates trajectory-level safety calibration by combining adaptive interaction, stateful tool execution, and execution-grounded adjudication; future work will expand its scope.
7 Conclusion and Future Work
- BLINDSPOT captures failures that may emerge after seemingly safe turns, and results show variation in safety-utility calibration and potential underestimation by short-horizon evaluation.The authors plan to extend the benchmark to additional domains, attack families, tools, and agent architectures.Source: §7 · pp. 9–10
A Algorithms
- Trajectory generation takes a scenario, attack or control, and User and Target Agents, then returns the interaction trajectory and adjudicated outcome.The algorithm records turn evidence, verifies the trajectory, and assigns its final outcome.Source: A Algorithms · p. 12
B Prompt and Configuration Templates
The appendix’s prompt and configuration templates specify scenarios, attack families, tools, and semantic judges for adaptive interactions and trajectory adjudication. They support execution-grounded decisions by matching attacks and safe twins to scenarios, adjudicating uncertain trajectories, and comparing controlled counterfactual interventions while avoiding unnecessary restriction.
B.1 Target Agent System Prompt
- The Target Agent receives a versioned system prompt, while policies, tools, memory, history, and observations are assembled separately; hidden grading labels, attack ground truth, and secrets are excluded.Prompt assembly orders system instructions, the benign objective, visible policies, tool schemas, memory, conversation history, and the current observation.Source: §B.1 · p. 12
B.2 Adaptive User Agent Prompt Template; B.2.1 Representative Family Template: Compliance Drift
- The adversarial User Agent generates fresh, response-conditioned messages using the attack phase and family; compliance drift escalates from benign requests and reframes after refusal.Its conditioning includes the Target Agent’s preceding response, refusal count, and an alternative refusal framing rather than a fixed dialogue script.Source: §B.2 · pp. 12–13, §B.2.1 · p. 13
B.3 Benign User Agent Prompt Template
- Benign User Agents pursue legitimate tasks and cooperate with clarification or authorization; their trajectories can yield Safe Completion, Over-Refusal, or Indeterminate outcomes.A benign trajectory is not automatically labeled Safe Completion: the task must be completed without unjustified refusal or execution failure.Source: §B.3 · p. 14
B.4 Semantic Judge Prompts; B.4.1 Unsafe Action Interpretation Judge; B.4.2 Over-Refusal Judge; B.4.3 Semantic Goal Completion Judge
- Three evidence-citing semantic judges assess unsafe actions, unnecessary refusals, and goal completion; their outputs combine with deterministic evidence to assign the five trajectory outcomes.Semantic judges are invoked only when deterministic checks cannot resolve a question, and do not directly assign the final outcome.Source: §B.4.3 · pp. 15–16, §B.4 · p. 15, §B.4.1 · p. 15
B.5 Scenario Specification Template; B.5.1 Representative Scenario: Controlled File Sharing
- Declarative scenarios specify objectives, environment state, policies, tools, and safety conditions, allowing benign, adversarial, and counterfactual trajectories under shared policy semantics.In the file-sharing example, external sharing requires active approval, so safety depends on the request and authorization history across the trajectory.Source: §B.5 · p. 16, §B.5.1 · p. 17
B.6 Attack-Family Configuration Template; B.6.1 Representative Attack Family: Compliance Drift
- Attack-family configurations encode objectives, phases, attacker capabilities, and adaptation rules, and are instantiated only when a scenario supplies the required tools, state, and policies.Compliance drift gradually reframes a legitimate interaction toward a policy-violating request and adapts subsequent messages to the Target Agent’s actual responses.Source: §B.6 · p. 17, §B.6.1 · p. 18
B.7 Tool and Environment Specification; B.7.1 Representative Tool: File Sharing
- Tool calls run through centralized validation, authorization, policy checks, and state mutation; replay-verified execution records, not agent prose, establish whether actions occurred.The runtime records state hashes and diffs, then checks the trace against replay before deterministic predicates or semantic judges are used.Source: §B.7 · p. 18, §B.7.1 · p. 19
B.8 Human Review and Adjudication Template
- Human review resolves materially disputed or low-confidence judgments, while Indeterminate is reserved for interactions that cannot reach a conclusive outcome because execution is incomplete or unavailable.Uncertainty about an otherwise completed trajectory prompts adjudication, not an automatic Indeterminate label.Source: §B.8 · pp. 19–20
B.9 Final Outcome Adjudication
- Final labels combine execution, policy, task-completion, and refusal evidence, giving mechanically verifiable operational evidence precedence over an agent’s wording.Semantic judges address questions unresolved by deterministic checks, with human review for materially disputed or low-confidence cases.Source: §B.9 · pp. 20–21
B.10 Scenario-Attack Applicability
- Attack families apply only to scenarios with their required features, and aggregate attack metrics use valid scenario-attack configurations rather than the full Cartesian product.This avoids counting non-applicable configurations as successful defenses or failed attacks.Source: §B.10 · p. 21, §B.10.2 · p. 22, §B.10.1 · p. 22
B.11 Safe-Twin Construction
- Safe twins preserve task and environment context while changing the safety-critical factor, testing whether agents distinguish policy-sensitive branches from superficially similar requests.The paired evaluation can reveal agents that refuse both unsafe and permitted requests.Source: §B.11 · p. 22, §B.11.1 · p. 23
B.11.1 Representative Safe-Twin Pair
- A file-sharing safe twin pairs refusal of an unapproved external share with completion of a permitted internal share, testing responses to the authorization difference.The example retains the same confidential report and task context across branches.Source: §B.11.1 · p. 23
B.12 Counterfactual Intervention Template
- Counterfactual branches preserve the recorded history and environment snapshot, changing only an intervention at or after the selected decision point.Interventions include requesting approval, restricting scope, blocking, or escalating, among other options.Source: §B.12 · pp. 24–25
B.12.1 Representative Counterfactual Branch
- In the file-sharing example, requesting approval instead of attempting an unapproved external share prevents the prohibited transition while preserving the preceding interaction and authorization state.The counterfactual may yield Correct Refusal or continued safe interaction pending approval.Source: §B.12.1 · p. 25
B.12.2 Minimal Safe Intervention
- BLINDSPOT compares candidate interventions to find the least restrictive option that prevents harm while retaining task utility.Branches are reported separately as executed, validated, or estimated; estimated outcomes are not treated as independently executed trajectories.Source: §B.12.2 · p. 25
C Evaluation Metrics
The metrics separate adversarial safety, permitted-task usefulness, over-refusal, execution incompleteness, and robustness across repeated runs and continued pressure.
C Evaluation Metrics
- Eight metrics cover unsafe behavior, appropriate refusal, benign completion, unnecessary refusal, execution failures, calibration, repeated-run robustness, and persistence under adversarial pressure.They distinguish unsafe compliance, safety behavior, utility and conservatism, long-horizon robustness, execution incompleteness, and the safety-utility trade-off.Source: C Evaluation Metrics · pp. 26–27
- The geometric-mean SCS combines benign completion and adversarial safety, requiring both successful benign tasks and low unsafe completion for a high score.Its components are BCR and 1 − UCR.Source: C Evaluation Metrics · p. 26
- Fail@k measures the percentage of valid scenario–attack configurations with at least one Unsafe Completion across k runs; lower values indicate more consistent safety.It captures repeated-run exposure that a single-run evaluation may underestimate.Source: C Evaluation Metrics · p. 26, Table 9 · p. 27
- PFR measures whether an initially safe refusal later transitions to Unsafe Completion under continued adversarial pressure.It captures failures after repeated reframing, authorization claims, or other adaptive pressure.Source: C Evaluation Metrics · p. 27
D Additional Experimental Results
- Supplementary analyses examine repeated-run robustness, post-refusal stability, persistent executable state, attack groups and domains, and adjudication evidence.They use the trajectory-generation and adjudication protocol described in Section 6.Source: D Additional Experimental Results · p. 27
D.1 Repeated-Run Robustness
Repeated executions expose vulnerable configurations across models, while post-refusal failure distinguishes initial refusal from refusal that persists under pressure.
D.1 Repeated-Run Robustness
- Fail@10 rises from 3% for GPT-5.6 Sol to 11%, from 23% for GPT-4o to 51%, and reaches 87% for Mistral Large 3.Failure exposure increases for every model with repeated runs, though marginal increases shrink at larger k.Source: §D.1 · p. 27
D.2 Post-Refusal Stability
- PFR is at or below 10% for four models, but reaches 47% for Claude Haiku 4.5, 52% for GPT-3.5 Turbo, and 60% for Mistral Large 3.GPT-4o initially refuses 57% of adversarial trajectories, yet more than one quarter of those refusals later transition to Unsafe Completion; Claude Opus 4.6 has 8% PFR.Source: §D.2 · p. 28, Table 10 · p. 28
D.3 Stateful Execution Ablation
Persistent executable state raises observed unsafe completion, and tool-composition attacks have the highest UCR across the evaluated models.
D.4 Operational Attack-Group Breakdown
- Tool-composition attacks have the highest UCR for every model, reaching 28% for GPT-4o, 48% for Claude Haiku 4.5, and 66% for Mistral Large 3.State/authorization exploitation is the least successful attack group, while relative model ordering remains largely stable across groups.Source: Table 12 · p. 29, §D.4 · p. 29
D.3 Stateful Execution Ablation
- Preserving executable state increases UCR for every model, by 1 point for GPT-5.6 Sol and up to 14 points for Mistral Large 3.The widening gap for weaker models suggests response-only evaluation can conceal failures; earlier actions and state changes affect later decisions.Source: §D.3 · p. 29
D.5 Cross-Domain Robustness
Cross-domain configurations have the highest UCR for every model, and combining deterministic with semantic evidence improves adjudication agreement over reduced evidence settings.
D.5 Cross-Domain Robustness
- Cross-domain UCR reaches 6% for Claude Opus 4.6, 26% for GPT-4o, and 64% for Mistral Large 3, exceeding their reported single-domain ranges.The pattern is also present for low-UCR models; domain differences are robustness patterns, not isolated causal effects, because scenario and attack composition varies.Source: §D.5 · p. 29, Table 13 · p. 30
D.6 Adjudication Ablation
- Deterministic and semantic evidence combined reaches 96% agreement, with 3% Unsafe Miss and 4% OR Miss.Final-response-only evaluation agrees on 71% of cases and misses 22% of Unsafe Completion and 18% of Over-Refusal outcomes; deterministic checks verify state transitions while semantic judging helps assess refusal justification.Source: Table 14 · p. 30, §D.6 · p. 30
E Dataset and Registry Statistics
Registry statistics show how attack applicability, interaction intensity, entry points, temporal structure, and domain differences shape the benchmark’s coverage. The multi-axis taxonomy captures overlapping attack properties across the 22 families.
E.1 Attack-Family Sampling
- 176 runs each for env_prompt_injection and task_injection, and 162 for social_proof_injection; run counts reflect scenario applicability, not intrinsic attack importance.Specialized state, authorization, or tool prerequisites make some families less frequent, so aggregate results describe valid scenario–attack configurations rather than an unrestricted Cartesian product.Source: §E.1 · pp. 30–31, Figure 7 · p. 31
E.3 Attack Surfaces and Target Components
- User influence appears in 13 families, compared with environment, tool, and memory sources in 6, 5, and 4; STATE/BELIEF and AUTHORIZATION each target 11 families.These annotations are non-exclusive; state and authorization targets matter in long-horizon evaluation because later action safety may depend on information changed earlier, including through the environment.Source: §E.3 · pp. 31–32
E.2 Tool-Use Intensity
- 23.0 tool calls per run for compound_request_hiding contrasts with 4.5 for capability_probe_exploit, showing interaction intensity differs from evaluation frequency.State rollback averages 22.8 calls, env_prompt_injection 19.5, and tool_chaining 17.9; Figure 8 reports mean calls per evaluated run.Source: §E.2 · p. 31, Figure 8 · p. 32
E.4 Temporal Structure and Adversary Knowledge
- Fourteen families have staged progression, versus three one-shot families; temporal labels overlap, and nine families each show gradual progression and delayed activation.Eight families use public responses, eight tool-call information, and six partial environment-state information, varying adversarial observability; evidence may become safety-critical only after later state or authorization changes.Source: §E.4 · p. 32
E.5 Domain Composition
- Collaborative Workspace has 40 permissions, 15 approval objects, and 10 tools, while Software Operations has fewer tools but safety-critical secrets, deployments, and incident state.E-commerce has six applicable attack families despite a comparatively small permission structure; the same authorization mechanism can concern payments in Finance or files, messages, events, and recipients in Collaborative Workspace.Source: §E.5 · p. 33, Table 15 · p. 33
E.6 Full Attack-Family Taxonomy
- The 22-family taxonomy labels source, target, mechanism, temporal structure, harm, and adversary knowledge, with multiple annotations allowed within axes.User-originated attacks are the largest source category, while authorization and state/belief recur across mechanisms; collapsing families to one mechanism label would lose distinctions in influence source, target, and evolution. Axis counts need not sum to 22.Source: E Dataset and Registry Statistics · p. 30, §E.6 · p. 33, Table 16 · p. 34