Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance.arXiv preprint arXiv:2508.20890, 2025.https://arxiv.org/ abs/2508.20890
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Security analysis of OpenClaw reveals composable RCE paths from LLM tool calls, invalid closed-world assumptions in exec allowlists, and plugin-based attacks that bypass runtime policy.
citing papers explorer
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Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening
Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
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A Security Analysis of the OpenClaw AI Agent Framework
Security analysis of OpenClaw reveals composable RCE paths from LLM tool calls, invalid closed-world assumptions in exec allowlists, and plugin-based attacks that bypass runtime policy.