arXiv:2605.11047v2 Announce Type: replace-cross Abstract: Agentic language-model systems increasingly rely on mutable execution contexts, including files, memory, tools, skills, and auxiliary artifacts, creating security risks beyond explicit user prompts. This paper presents DeepTrap, an automated framework for discovering contextual vulnerabilities in OpenClaw. DeepTrap formulates adversarial context manipulation as a black-box trajectory-level optimization problem that balances risk realization, benign-task preservation, and stealth. It combines risk-conditioned evaluation, multi-objective
The rapid acceleration in the development and deployment of agentic AI systems necessitates immediate focus on security vulnerabilities that extend beyond traditional prompt injection.
Sophisticated security frameworks are critical for the safe and reliable deployment of AI agents, directly impacting their commercial viability and societal acceptance.
The focus of AI security expands from prompt-level vulnerabilities to broader contextual execution environments, requiring new evaluation methodologies.
- · AI security firms
- · Developers of robust AI agent platforms
- · Organizations adopting secure AI agents
- · AI agent developers ignoring execution context security
- · Organizations deploying vulnerable AI agents
- · Attackers relying on basic prompt injection
Automated discovery of contextual vulnerabilities improves the security posture of advanced AI agent systems.
Enhanced security frameworks accelerate the adoption and integration of AI agents into critical workflows, validating and potentially expanding the 'AI agents' narrative.
The development of highly secure and autonomous AI agents could further accelerate the collapse of white-collar workflows, creating new economic structures.
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Read at arXiv cs.AI