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GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents

Jie Zhang, Jiyan He, Qiannan Zhu, Shuxin Zheng, Weiming Zhang, Wenbo Zhou, Yanxi Wang, Yu Shi, Zhiling Zhang

GUIGuard-Bench shows current models detect private information in GUI screenshots but struggle with precise localization, category recognition, risk assessment, and judging task necessity.

arxiv:2601.18842 v3 · 2026-01-26 · cs.CR · cs.AI · cs.CV

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Claims

C1strongest claim

Our results show that current models can often detect whether a screenshot contains private information, but they struggle with fine-grained localization, category recognition, risk assessment, and task-necessity judgment. We also find that closed-source models, exemplified by Claude Sonnet 4.6, can maintain largely consistent planner semantics in Android environments after privacy protection is applied.

C2weakest assumption

The human-provided region-level annotations for privacy bounding boxes, categories, risk levels, and task necessity are accurate and representative of real-world GUI agent privacy risks across the collected trajectories.

C3one line summary

GUIGuard-Bench is a new benchmark with annotated GUI screenshots that measures privacy recognition, planning fidelity under protection, and utility impact for trajectory-based GUI agents.

References

126 extracted · 126 resolved · 5 Pith anchors

[1] https://blog 2025
[2] Surfer 2: The next generation of cross- platform computer use agents 2025
[3] Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated 2025 · doi:10.18653/v1/2025
[4] Ghostei-bench: Do mobile agents resilience to environmental injection in dynamic on-device environments? 2025
[5] Product webpage 2025

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4 papers in Pith

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First computed 2026-05-18T03:09:24.270098Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

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41a870b3685c492fea5f55db386afc73ecf0d8eb0bcfde704d8971a5b8d6b2aa

Aliases

arxiv: 2601.18842 · arxiv_version: 2601.18842v3 · doi: 10.48550/arxiv.2601.18842 · pith_short_12: IGUHBM3ILRES · pith_short_16: IGUHBM3ILRES72S7 · pith_short_8: IGUHBM3I
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IGUHBM3ILRES72S7KXNTQ2X4OP \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 41a870b3685c492fea5f55db386afc73ecf0d8eb0bcfde704d8971a5b8d6b2aa
Canonical record JSON
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