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LogJack: Indirect Prompt Injection Through Cloud Logs Against LLM Debugging Agents

arXiv Security Archived Apr 20, 2026 ✓ Full text saved

arXiv:2604.15368v1 Announce Type: new Abstract: LLM debugging agents that consume cloud logs and execute remediation commands are vulnerable to indirect prompt injection through log content. We present LogJack, a benchmark of 42 payloads across 5 cloud log categories, and evaluate 8 foundation models under 3 prompt conditions with 5 independent trials each (n = 160 per model per condition on 32 attack payloads). Under the active condition, verbatim command execution rates range from 0% (Claude S

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    Computer Science > Cryptography and Security [Submitted on 15 Apr 2026] LogJack: Indirect Prompt Injection Through Cloud Logs Against LLM Debugging Agents Harsh Shah LLM debugging agents that consume cloud logs and execute remediation commands are vulnerable to indirect prompt injection through log content. We present LogJack, a benchmark of 42 payloads across 5 cloud log categories, and evaluate 8 foundation models under 3 prompt conditions with 5 independent trials each (n = 160 per model per condition on 32 attack payloads). Under the active condition, verbatim command execution rates range from 0% (Claude Sonnet 4.6) to 86.2% (Llama 3.3 70B). Passive instructions ("do not execute fixes") reduce most models to 0% but Llama still executes at 30.0%. Remote code execution via curl | bash succeeds on 6 of 8 models. Guardrails from AWS, GCP, and Azure largely fail to detect log-embedded injections-Azure Prompt Shield detected only the most obvious payload (1/32), while GCP Model Armor detected none-though they detect identical payloads in isolation. We also observe a novel "sanitize and execute" behavior where a model detects and removes an obvious malicious component but still executes the remaining injected command. Benchmark and harness available at this http URL. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.15368 [cs.CR]   (or arXiv:2604.15368v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.15368 Focus to learn more Submission history From: Harsh Shah [view email] [v1] Wed, 15 Apr 2026 04:32:36 UTC (10 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 20, 2026
    Archived
    Apr 20, 2026
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