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BARC-Bench: Benign Asks, Risky Consequences

Kangkang Li, Kelvin Luu, Lily Ma, Tu Trinh, Ying Liu, Mohamed Elfeki

arXiv:2610.32024v1cs.AI

TL;DR

Benign workplace requests can be underspecified and still implicate safety boundaries; BARC-Bench tests this problem with 110 tasks across nine simulated environments. Across four model configurations, safety was inconsistent: even GPT-6 Astra violated on 27.3% of tasks across five attempts.

  • Problem

    Routine workplace requests can be completed while leaking sensitive information, and existing evaluations of task completion and harmful-request refusal do not center these routine workflow consequences.

    Source: §1 · pp. 1–2

  • Method

    BARC-Bench evaluates agents on 110 benign workplace requests across nine synthetic environments, with tasks developed through a multi-stage AI-assisted pipeline.

    Source: §3.2 · p. 6, §7 · p. 11

  • Results

    Models were not consistently safe; even GPT-6 Astra produced at least one violation across five attempts on 27.3% of tasks.

    Source: §7 · p. 11

  • Takeaways & Limitations

    Evaluations should measure whether agents complete requests consistently safely, not only whether they can complete them.

    Source: §7 · p. 11

    Each task covers a single delegation turn and often one hazard, unlike real workflows that accumulate context across many turns.

    Source: §6 · pp. 10–11

Abstract

from arXiv · show

Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks. To study these risks, we introduce BARC-Bench, a collection of 110 tasks across nine simulated workspace environments where benign requests leave openings for unsafe actions. In this benchmark, we evaluate agent behavior against safety boundaries established by workspace records, implicit environmental context, and unstated social norms. For example, an agent asked to schedule off-boarding discussions must arrange the meeting without prematurely revealing an employee's resignation through a shared calendar invitation. Evaluating four frontier model configurations across five attempts per task reveals that models are not consistently safe: the share of tasks completed safely on all five attempts ranges from 9.1% to 72.7%, with even the strongest configuration producing a safety violation on 27.3% of tasks. These results highlight an asymmetric operational risk: while models frequently complete complex workflows, their inability to consistently respect unstated boundaries presents a substantial barrier to safe autonomous delegation.

1 Introduction

Routine workplace requests can be completed while still causing harm when an agent mishandles sensitive information or context. BARC-Bench examines whether agents can pursue legitimate goals without crossing safety boundaries in realistic workflows.

1 Introduction

  • A meeting about an employee’s departure can be scheduled successfully yet leak sensitive information through a descriptive title on a shared calendar.The task is to arrange the meeting without prematurely revealing the departure.Source: §1 · p. 1, Figure 1 · p. 2
  • Benign requests may be underspecified or rely on incorrect assumptions, and choices about an action’s content or audience can make otherwise allowed work unsafe.Relevant boundaries may be explicit, implicit across records, or socially understood.Source: §1 · p. 2
  • 110 tasks across nine simulated environments ground a benchmark of benign workplace requests in documents, communication histories, and other workspace artifacts.Tasks use human-authored and curated seeds with AI-assisted expansion.Source: §1 · pp. 2–3
  • The paper analyzes safety across task characteristics and compares failure patterns between models, complementing evaluations of task completion, refusal, and prompt-injection defense.It evaluates agents through their actions, distinguishing safety violations, safe completions, and deferrals.Source: §1 · pp. 2–3

2 Related Work

Prior agent evaluations cover realistic tool use, adversarial safety, social norms, ambiguity, and privacy. BARC-Bench focuses on harmful consequences that can arise while agents pursue benign workplace requests.

2 Related Work

  • Tool-agent benchmarks have expanded from isolated API calls to sustained, multi-turn work in stateful retail, airline, software-company, web, and desktop settings.Examples include τ-bench, TheAgentCompany, WebArena [24], and OSWorld [20].Source: §2 · p. 3
  • Agent-safety work has largely examined harmful requests [2] and prompt injection [5], while BARC-Bench studies legitimate asks whose execution can still harm users or others.Other tool-use and personal-agent safety work includes MCP use [25], interaction risks [23], and OpenClaw agents [10].Source: §2 · p. 3
  • Related work examines social expectations [6] [13] [1], clarification under ambiguity [16], and contextual privacy [11] [15]; CI-Work [7] focuses on sensitive enterprise information, while BARC-Bench also covers destructive changes, unsupported claims, and unauthorized actions.BARC-Bench uses fixed environments with connected records, enabling study of evidence gathering alongside judgment about unsafe actions.Source: §2 · pp. 3–4

3 Dataset

BARC-Bench pairs benign workplace requests with connected simulated environments and task-specific safety judgments. Its construction combines human-authored seeds, AI-assisted expansion, and human review.

3 Dataset

  • The dataset construction has three stages: build workplace environments, author tasks within them, and expand those tasks into related instances.Environments provide organizational context for decisions arising during ordinary requests.Source: §3 · p. 4
  • Each task presents a workplace request for an agent acting on an employee’s behalf, using standing instructions and connected tools; a task-specific rubric evaluates its actions.Benign intent does not guarantee that the requested action is safe, since prompts may omit context or contain mistaken assumptions.Source: §3 · p. 4

3.1 Environment and Universe Generation

  • Each environment connects people, roles, communication histories, calendars, documents, and operational records through tools for investigation and action.Records may reference related information across services, such as an email, contract, and colleague discussion.Source: §3.1 · p. 4
  • Environments average 817.3 accessible artifacts across 5–9 servers, with sizes ranging from 487 to 1270 artifacts.Universe construction combines a human-drafted canonical ledger, model-assisted artifact generation, iterative human and model review, and final human quality control.Source: §3.1 · p. 5

3.2 Task Creation

  • Tasks are characterized by workflow and safety judgment: whether the request conflicts with a boundary and how the agent can identify that boundary.Boundaries may be explicit in records, implicit across related artifacts, or social; conflicting requests call for adaptation or clarification.Source: §3.2 · pp. 5–6
  • Task creation begins with manually authored seeds and expands them through a multi-stage agent pipeline that plans safety concerns, writes authentic requests, and checks realism, objectivity, and fairness.Generated instances need not represent distinct safety mechanisms; a final human audit filters the collection to 110 tasks.Source: §3.2 · p. 6, Figure 2 · p. 6

4 Evaluation

The evaluation measures safety over five attempts per task, treating safe completion and deferral as acceptable outcomes while tracking violations. A contextual judge assesses trajectories and environment state, and four model configurations are tested.

4 Evaluation

  • Each configuration receives five attempts per task, in which agents may inspect records, use tools, and take multiple actions to pursue a benign request.The evaluation asks whether they avoid crossing a safety boundary without an explicit warning.Source: §4 · p. 7
  • Attempts are classified as safe completion, deferral before any unsafe action, or unsafe if at least one harmful action occurs.Deferral is allowed for safety measurement, although it does not count as task completion or success.Source: §4 · p. 7

4.1 Safety Judge

  • Safety pass5 is the fraction of tasks with no violation in all five attempts, distinguishing consistent safety from a metric requiring only one safe attempt.The judge considers actions in context; later completion or deferral does not override an earlier violation.Source: §4 · p. 7, §4.1 · p. 7, Figure 3 · p. 8
  • GPT-5.6 Sol judges each attempt against a contextual task rubric using the prompt, trajectory, final response and artifacts, and final environment state.On 300 trajectories, judge agreement was 88.3% with unanimous human opinion and 90.0% with majority opinion; average pairwise human agreement was 93.3%.Source: §4.1 · p. 7

4.2 Agent Configurations

  • The study evaluates GPT-6 Astra, Claude-Fable 5.1, Gemini 3.8-Flash, and DeepSeek V4-Pro using the OpenCode harness and AgentEnv Framework.Each model used its default thinking budget and completed five attempts per task.Source: §4.2 · p. 8

5 Results

Across repeated attempts, models differ substantially in safety consistency, and safety patterns vary by workflow and boundary type. Failure analyses show distinct difficulties with explicit rules, implicit environmental boundaries, and social norms.

5.1 Are models consistently safe?

  • 72.7% of tasks were all-safe for GPT-6 Astra, compared with 37.3% for Claude-Fable 5.1, 23.6% for Gemini 3.8-Flash, and 9.1% for DeepSeek V4-Pro.All-safe requires avoiding a safety violation on all five attempts; this measures safety consistency, not unsafe-action likelihood or successful task completion.Source: §5.1 · p. 8, Table 3 · p. 9
  • 46.4% for Gemini-3.8-Flash and 51.8% for Deepseek V4-Pro on pass@k reversed their ordering relative to passk safety, showing that one safe attempt can obscure consistency.Source: §5.1 · p. 8

5.2 Do violations differ across conflicting and non-conflicting boundaries?

  • For non-conflicting requests, all four models were more consistently safe on file than communication tasks; GPT-6 Astra’s all-safe rates were 96.4% and 65.6%, respectively.Source: §5.2 · p. 9
  • For requests conflicting with safety boundaries, GPT-6 Astra remained relatively safe on file operations, Claude-Fable 5.1 showed the opposite trend, and Gemini-3.8-Flash and DeepSeek V4-Pro were not consistently safe on any task type.Source: §5.2 · p. 9

5.3 How do failures vary across safety boundaries?

  • For explicit environmental boundaries, 50% of GPT-6 Astra’s failures involved retrieving a restriction but still violating it; corresponding shares were 41%–44% for other models.Table 4 groups task-level failure annotations; its percentages use each model’s failed-task denominator, including unclassified cases, rather than all-five-safe rates.Source: §5.3 · p. 9, Table 4 · p. 10
  • For implicit environmental boundaries, failures across all models mostly reflected not inferring the boundary despite having the relevant artifacts.Source: §5.3 · p. 10
  • For implicit social norms, agents often had the necessary information but complied with the request while ignoring the boundary.Source: §5.3 · p. 10

6 Limitations

The benchmark’s safety judgments reflect reviewers’ contextual norms rather than an objective definition of safety. Its scope and primary metric also leave important aspects of real-world workflow performance uncharacterized.

6 Limitations

  • Rubrics encode contextual judgments that may vary across people and organizations, so the benchmark reflects reviewer norms rather than an objective safety definition.The authors report reviewer agreement to characterize this variation.Source: §6 · p. 10
  • Reviewers and authors were predominantly researchers and engineers in a United States-based workplace, and the tasks’ norms may not reflect the global population.Source: §6 · p. 10
  • Tasks cover only a single delegation turn and often a single hazard, unlike real workflows that extend across turns and accumulate context.The authors note that agent behavior may differ when absorbing information or maintaining context.Source: §6 · pp. 10–11
  • The primary metric measures harm avoidance rather than task completion, so the paper does not fully characterize systems that are both safe and useful.Safe completion is measured more coarsely and received less human scrutiny than violations.Source: §6 · p. 11

7 Conclusion

BARC-Bench evaluates harmful actions arising from benign workplace requests across synthetic environments. Its results show inconsistent safety and failures to apply relevant restrictions, motivating measurement of consistent safety alongside task completion.

7 Conclusion

  • BARC-Bench evaluates potential harmful actions from 110 benign workplace requests across nine synthetic environments.Source: §7 · p. 11
  • 27.3% of tasks had at least one safety violation across five attempts for the safest model, GPT-6 Astra.Across four model configurations, BARC-Bench measures consistency with an all-safe metric.Source: §7 · p. 11
  • Failures often occur even when agents find a relevant restriction, because they do not properly use that information.Safe handling requires finding, inferring, and applying restrictions, respecting permissions, and recognizing unstated boundaries around sensitive information.Source: §7 · p. 11
  • A single unstated boundary violation can cause severe harm, and current models do not yet act consistently safely on users’ behalf.The authors argue agents should gather information and exercise context-sensitive restraint reliably.Source: §7 · p. 11

AI Use Statement

AI models assisted with generating environments and tasks, drafting and revising the paper, and producing plots and tables.

AI Use Statement

  • AI models helped generate environments and tasks, brainstorm and structure the writing, and suggest revisions.Source: AI Use Statement · p. 11
  • AI was also used to help generate plots and tables, and AI judges were used as described in Section 4.1.Source: AI Use Statement · p. 11
  • The authors describe AI assistance in writing and revising the paper.Source: AI Use Statement · p. 11

Ethics Statement

BARC-Bench evaluates potentially harmful actions in synthetic environments, but benchmark performance does not certify a model as safe for deployment.

Ethics Statement

  • The benchmark uses synthetic scenarios that can involve sensitive information, authorization, and destructive tasks; performance is not deployment safety certification.Its contextual judgments may not be universal across cultures.Source: Ethics Statement · p. 11

A.1 Manual Authoring

BARC-Bench tasks were developed from human-authored seeds, expanded through a synthetic pipeline, and reviewed for meaningful, executable safety decisions. The process produced 63 retained seeds and 47 generated instances, while distinguishing repeated scenarios from newly discovered safety mechanisms.

A.1 Manual Authoring

  • Human authors identified each task’s goal, safe route, harmful action, and supporting context, then revised prompts and rubrics using development rollouts.Tasks were required to stump GPT-5.6 Sol two-thirds of the time; a second reviewer screened tasks that met the initial quality and difficulty bars.Source: §A.1 · p. 15

A.2 Synthetic Pipeline

  • Generated requests can vary audience, workflow, wording, and output, so multiple instances may test the same decision rather than represent new safety mechanisms.The authors distinguish generated instances from human-authored seeds instead of treating the expanded task count as independently discovered scenarios.Source: §A.2 · p. 15
  • The synthetic pipeline produced 47 additional instances from 63 retained seeds, yielding 110 tasks, through planning, prompt writing, and validation.The planner explores with read-only tools; a writer frames a realistic benign request, and a validator checks grounding, legitimacy, fairness, and whether the task can be done safely.Source: §A.2 · pp. 15–16

A.3 Task Validation

  • Human reviewers assessed whether each task presented a meaningful safety decision, exposed it in the environment, and involved a legitimate goal and consequential action.For conflicting requests and records, reviewers considered the user’s knowledge and the workplace approval process; an older status alone did not establish that the user was wrong.Source: §A.3 · p. 16
  • Execution checks verified that instructions, supporting records, and relevant actions were available and observable across native and task-mounted files.The final review involved three additional reviewers to avoid bias from the initial auditor; missing evidence or mismatched action tracking required repair.Source: §A.3 · p. 16

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