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REVIEW 3 major objections 1 minor 15 references

Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?

T0 review · 3 major / 1 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Eleven of fifteen frontier computer-use agents leak private information in more than half of contextual integrity scenarios.

desk verdict The paper's main contribution is AgentCIBench plus the 67.9% average leakage result on 15 agents, but the three failure modes' match to real deployments is the part that needs checking. read the letter →

arxiv 2606.23189 v1 pith:JEVZO7YO submitted 2026-06-22 cs.AI cs.CL

Anmol Goel , Iryna Gurevych This is my paper · ORCID
classification cs.AIcs.CL
keywords computer-useagentscontextualintegrityprivacyleakageAgentCIBenchAIpersonaldatafailuremodes
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents AgentCIBench as a way to measure whether computer-use agents respect contextual integrity when operating across a user's personal applications. It defines three concrete failure modes that cause inappropriate information disclosure: visual co-location of prohibited items, oversharing under vague prompts, and sending material to mismatched recipients. Testing fifteen current agents shows that eleven exceed a 50 percent leakage rate, averaging 67.9 percent, with the same pattern appearing when agents run full end-to-end tasks. A reader would care because these agents are already being built to act inside email, calendars, and to-do lists that contain sensitive personal data.

What carries the argument

AgentCIBench, an evaluation harness that converts contextual integrity risks into deterministically scored scenarios for visual co-location, task-ambiguity overshare, and recipient misalignment.

What would settle it

An agent version or new architecture that consistently keeps leakage below 20 percent across all AgentCIBench scenarios while still completing the same tasks would directly contradict the reported failure rates.

Watch

Extended reading notes

Core claim

Frontier computer-use agents routinely violate contextual integrity by disclosing information that is inappropriate to the immediate task context. The authors introduce AgentCIBench, an evaluation harness that converts this risk into executable scenarios covering visual co-location, task-ambiguity overshare, and recipient misalignment. On this benchmark, eleven of fifteen agents leak on more than half the cases with an average leakage of 67.9 percent, and identical failures continue when the same agents act end-to-end inside the environment to finish the assigned task.

Load-bearing premise

The three failure modes tested in AgentCIBench capture the privacy risks that would actually appear when users run these agents on their own devices and data.

Editorial extensions

If this is right

  • The same leakage patterns appear when agents execute complete tasks inside the actual environment rather than in isolated prompts.
  • Cross-application access in personal tools creates systematic opportunities for inappropriate disclosure.
  • Current agents require additional safeguards before they can be trusted with real user data.
  • Releasing the benchmark is intended to drive development of agents that pass contextual integrity checks before deployment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Widespread use of these agents without context controls could produce routine unintended sharing of personal details across apps.
  • Contextual integrity testing could become a routine pre-deployment requirement alongside capability benchmarks.
  • Agent designs may need explicit internal representations of context boundaries rather than relying on general instruction following.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces AgentCIBench, an evaluation harness that converts contextual-integrity risks for computer-use agents (CUAs) into executable, deterministically scored scenarios. It targets three failure modes—visual co-location, task-ambiguity overshare, and recipient misalignment—and reports that 11 of 15 evaluated frontier agents leak on more than 50% of scenarios (average leakage 67.9%), with the same failures persisting when agents complete tasks end-to-end in the environment. The benchmark is released publicly.

Significance. If the empirical measurements hold, the work identifies a concrete and previously under-examined privacy risk arising from cross-application access by capable CUAs. The public release of AgentCIBench supplies a reproducible test harness that can serve as a pre-deployment safety check, which is a constructive contribution to the field.

major comments (3)
  1. [Abstract and Evaluation] Abstract and Evaluation section: the reported aggregate leakage rate of 67.9% and the claim that 11 of 15 agents exceed 50% leakage are presented without stating the total number of scenarios, the number per failure mode, any statistical significance tests, or the precise deterministic scoring rules for leakage. These omissions prevent verification of the central quantitative claim.
  2. [Introduction and Evaluation] Introduction and Evaluation: the three failure modes are described as 'common,' yet no user studies, usage logs, or coverage argument is supplied to establish that visual co-location, task-ambiguity overshare, and recipient misalignment are representative of the privacy risks that would actually arise in typical CUA deployments on personal devices. This assumption is load-bearing for the broader conclusion that frontier agents are 'careless' in real contexts.
  3. [Methods] Methods: no information is given on inter-rater reliability for scenario construction or on how ground-truth non-leakage is ensured and verified. Without these details the benchmark's internal validity cannot be assessed.
minor comments (1)
  1. [Abstract] The phrase 'surprisingly high failure rate' in the abstract is interpretive; a neutral statement of the observed percentages would be more precise.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below, indicating where we will revise the manuscript to improve clarity and transparency.

read point-by-point responses

  1. Referee: [Abstract and Evaluation] Abstract and Evaluation section: the reported aggregate leakage rate of 67.9% and the claim that 11 of 15 agents exceed 50% leakage are presented without stating the total number of scenarios, the number per failure mode, any statistical significance tests, or the precise deterministic scoring rules for leakage. These omissions prevent verification of the central quantitative claim.

    Authors: We agree these details are necessary for verification. The manuscript's Evaluation section defines the three failure modes and the deterministic scoring procedure (an agent leaks if it outputs prohibited information in the target context), but we will explicitly report the total number of scenarios, the per-mode counts, and any applicable statistical tests in a revised version of the Evaluation section and abstract. revision: yes

  2. Referee: [Introduction and Evaluation] Introduction and Evaluation: the three failure modes are described as 'common,' yet no user studies, usage logs, or coverage argument is supplied to establish that visual co-location, task-ambiguity overshare, and recipient misalignment are representative of the privacy risks that would actually arise in typical CUA deployments on personal devices. This assumption is load-bearing for the broader conclusion that frontier agents are 'careless' in real contexts.

    Authors: The three modes were selected after systematic observation of CUA interactions with cross-application UIs. We will add an explicit coverage argument in the Introduction that links each mode to documented CUA capabilities (e.g., screenshot access, ambiguous natural-language instructions, and multi-recipient actions) and will discuss the lack of large-scale usage studies as a limitation of the current benchmark design. revision: partial

  3. Referee: [Methods] Methods: no information is given on inter-rater reliability for scenario construction or on how ground-truth non-leakage is ensured and verified. Without these details the benchmark's internal validity cannot be assessed.

    Authors: We will expand the Methods section to describe the scenario-construction process, including how ground-truth non-leakage labels were established through author review and deterministic checks against the contextual-integrity rules. Formal inter-rater reliability statistics were not computed; we will note this and treat it as a methodological limitation. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical benchmark evaluation with direct measurements

full rationale

This paper introduces AgentCIBench as an evaluation harness defining three specific failure-mode scenarios (visual co-location, task-ambiguity overshare, recipient misalignment) and reports direct empirical leakage rates from running 15 agents on those scenarios. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the derivation of the central claims. The reported statistics (11/15 agents >50% leakage, avg 67.9%) are measurements against the explicitly constructed benchmark rather than quantities that reduce to the benchmark definition by construction. The representativeness concern raised by the skeptic is a question of external validity, not circularity in the paper's internal logic.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Empirical benchmark paper; contains no free parameters, mathematical axioms, or invented entities.

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Cite this review

Pith. "Pith review of Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?." pith.science (2026). https://pith.science/paper/JEVZO7YO

@misc{pith2026260623189,
  author       = {Pith},
  title        = {Pith review of: Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JEVZO7YO}},
  note         = {Machine review of arXiv:2606.23189}
}
read the original abstract

Computer-use agents (CUAs) now act on a user's behalf across personal applications such as email, calendars, and to-do lists. This cross-application access is useful, but it also creates a privacy risk that has been largely overlooked: when an agent works in one context, it can pull in information from another that is inappropriate in that context. Hence, we introduce AgentCIBench, an evaluation harness that turns this risk into executable, deterministically scored scenarios. We target three common failure modes in CUAs: visual co-location, where the agent pulls in prohibited items that sit next to the task target in the UI; task-ambiguity overshare, where the agent dumps dense personal state in response to an under-specified prompt; and recipient misalignment, where the agent sends content to an addressee for whom it is inappropriate. We evaluate 15 frontier agents and find a surprisingly high failure rate: 11 of 15 leak on more than 50% of scenarios, with an average leakage of 67.9%, and the same failures persist when agents act end-to-end in the environment to complete the task. We release AgentCIBench to encourage the development of safer computer-use agents and position contextual disclosure testing as a pre-deployment safety check.

Figures

Figures reproduced from arXiv: 2606.23189 by the authors.

Figure 1
Figure 1. Inappropriate Disclosure in CUAs. The user asks the agent to send the office supplies list; with access to the open shopping list and messenger. The agent sur￾faces personal items alongside the work-relevant ones in the reply to a colleague. open-source systems, including Claude Cowork (Anthropic, 2026), OpenAI Codex (OpenAI, 2026), OpenClaw (OpenClaw, 2026), and OpenCUA (Wang et al., 2025). These agents read person… view at source ↗
Figure 2
Figure 2. AGENTCIBENCH scenario-generation pipeline. An MCTS search mutates seed scenarios into new candidates using an LLM, runs proxy agents on them, and retains the high-reward, non-duplicate ones for the final pool. evaluated in our experiments are never used during scenario generation. Scenario-surfacing engine. We use Monte Carlo Tree Search (MCTS) (Kocsis and Szepesvári, 2006; Coulom, 2006), following Pulipaka et al. (… view at source ↗
Figure 3
Figure 3. Utility vs. engagement-conditioned leakage. Marker size indicates refusal rate and color indicates model family. Dashed lines at U=75% and Leng=50% define four regions: only Claude-Opus-4.7 falls in the desirable “capable & careful” quadrant (high utility, low leakage), while several top-utility agents cluster in the “capable but careless” upper right. GPT-5.4 achieves comparatively low leakage mainly via a high ref… view at source ↗
Figures from the paper (24 more)
Figure 4
Figure 4. Figure 4: End-to-end results for Claude-Opus-4.7 and Claude-Sonnet-4.6 in the OpenApps UI envi￾ronment, compared against their state-grounded base￾lines. On the runs the agents complete, engagement￾conditioned leakage rises to 42.9% for Opus and 80.0% for Sonnet, at or above the…
Figure 5
Figure 5. Figure 5: Real end-to-end trajectory of Claude-Opus-4.7, interacting with the environment. The agent cycles between OpenTodos and Nora’s message thread for 13 steps, then at step 14 fills the message box with all 12 to-do items and sends it; four inappropriate items (plumber, ve…
Figure 6
Figure 6. Figure 6: Three defenses against the baseline agent, averaged across Claude-Opus-4.7, GPT-5.4, and DeepSeek-v4-Pro. Leakage is the engagement￾conditioned rate Leng (leak rate among non-refused runs) to remove the refusal-rate confound. Every de￾fense lowers leakage and raises ut…
Figure 7
Figure 7. Figure 7: One seed scenario. The must_share and must_not_share sets are present in initial_states and visible to the agent; the harness scores the agent’s output deterministically against these sets. scores each rollout with the hybrid reward de￾scribed in Appendix E, and averag…
Figure 8
Figure 8. Figure 8: One scenario from the visual co-location (VCL) family: the prohibited item sits adjacent to the task target [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: One scenario from the task-ambiguity overshare (TAO) family: an under-specified prompt invites a dump [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: One scenario from the recipient-misalignment (RMA) family: content from a family context bleeds into [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Two surfaces from a single end-to-end vi [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Raw leakage vs. engagement-conditioned leakage for the fifteen agents. Claude-Opus-4.7 stays near the diagonal (restraint, 1.7% refusal), while GPT-5.4 shows a large vertical gap (41.9% refusal masking leakage). tion: Claude-Opus-4.7 (already-low leakage), GPT-5.4 (re…
Figure 13
Figure 13. Figure 13: Bump chart of utility (left) vs. disclosure [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: Engagement-conditioned leakage per agent [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Behaviour decomposition: each (model, scenario) cell is classified by whether the agent completed [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: The agent treats the under-specified “summarize my list” as a mandate to dump every item, including a [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: A personal draft sitting in the same editor session as the auth files is dragged along into a colleague-facing [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]
Figure 18
Figure 18. Figure 18: The user asks only for time blocks, but the agent surfaces full event titles (medical, HR, and a competitor [PITH_FULL_IMAGE:figures/full_fig_p023_18.png]
Figure 19
Figure 19. Figure 19: Same scenario as Figure [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: Same scenario as Figure [PITH_FULL_IMAGE:figures/full_fig_p025_20.png]
Figure 21
Figure 21. Figure 21: System prompt used by the mutator at every MCTS step. [PITH_FULL_IMAGE:figures/full_fig_p026_21.png]
Figure 22
Figure 22. Figure 22: Per-strategy instructions appended to the mutation system prompt. [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]
Figure 23
Figure 23. Figure 23: System prompt for the three open-weight proxy agents used during MCTS rollouts. [PITH_FULL_IMAGE:figures/full_fig_p027_23.png]
Figure 24
Figure 24. Figure 24: System prompt for the LLM judge. Restrictive defense You are a personal UI agent operating apps the user owns. The user trusts you with their entire screen, so you must be deliberately selective about what you read and what you reproduce. Strict rules: 1. Read only th…
Figure 25
Figure 25. Figure 25: Restrictive defense prompt. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_25.png]
Figure 26
Figure 26. Figure 26: Rubric-informed defense prompt. Recipient-typed defense You are a personal UI agent. Before drafting any outgoing artifact, you must briefly think through the recipient model. Internally (do not surface this reasoning to the user) answer: 1. Who, exactly, will read th…
Figure 27
Figure 27. Figure 27: Recipient-typed defense prompt. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_27.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Reviewed June 26, 2026 · model on record in the stance chip above.