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Ghost in the Context: Measuring Policy-Carriage Failures in Decision-Time Assembly

Igor Santos-Grueiro

Decision-time context assembly in LM agents is a measurable control-path element that can be partially hardened.

arxiv:2605.12535 v1 · 2026-05-02 · cs.CR

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Claims

C1strongest claim

Decision-time context assembly is therefore a measurable part of the control path that can be partially hardened.

C2weakest assumption

That the specific context assembly policies tested (truncation, structured-compaction) are representative of those used in practical LM agent deployments, and that the constraint respect judgments accurately reflect policy carriage without evaluator bias.

C3one line summary

Policy directives can be lost during context assembly in language model agents, leading to unprompted policy violations that SafeContext can partially prevent.

References

44 extracted · 44 resolved · 5 Pith anchors

[1] Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun 2024 · doi:10.18653/v1/2024.acl-
[2] Gormley, and Graham Neubig 2025 · doi:10.18653/v1/2025.naacl-long.605
[3] Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents 2024 · doi:10.52202/079017-2636
[4] Shen Dong, Shaochen Xu, Pengfei He, Yige Li, Jiliang Tang, Tianming Liu, Hui Liu, and Zhen Xiang. 2025. Memory Injection Attacks on LLM Agents via Query- Only Interaction. InAdvances in Neural Informa 2025
[5] Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. 2024. RULER: What’s the Real Context Size of Your Long-Context Language Models?. In 2024
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First computed 2026-05-18T03:10:02.475963Z
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b8911045a65d56638890feff852517c3e6bbbc962bb88f0ebe235a99ae1af8b7

Aliases

arxiv: 2605.12535 · arxiv_version: 2605.12535v1 · doi: 10.48550/arxiv.2605.12535 · pith_short_12: XCIRARNGLVLG · pith_short_16: XCIRARNGLVLGHCEQ · pith_short_8: XCIRARNG
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XCIRARNGLVLGHCEQ737YKJIXYP \
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Canonical record JSON
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