arXiv:2512.23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance on dynamic web content, however, makes them vulnerable to prompt injection attacks: adversarial instructions hidden in interface elements that persuade the agent to divert from its original task. We introduce the Task-Redirecting Agent Persuasion Benchmark (TRAP), a benchmark for studying how persuasion techniques misguide autonomous web agents on realistic tasks. Across six frontier model
The increasing deployment of LLM-powered web agents for critical tasks makes their vulnerability to adversarial manipulation a pressing concern, necessitating immediate research and defensive measures.
This development highlights a fundamental security flaw in autonomous AI systems, posing significant risks to data integrity, operational reliability, and user trust across various web-based applications.
The focus shifts from merely building capable AI agents to ensuring their robustness against malicious persuasive techniques, requiring new security protocols and validation benchmarks for agent deployment.
- · AI security researchers
- · Cybersecurity firms
- · AI red teaming specialists
- · Organizations developing secure agent frameworks
- · Unsecured AI agent developers
- · Users relying on unhardened web agents
- · Businesses deploying agents without robust prompt injection defenses
The benchmark will drive the development of more resilient web agents capable of identifying and resisting prompt injection attacks.
Increased scrutiny and regulation around the security and trustworthiness of autonomous AI systems will emerge, especially in sensitive applications.
A new competitive landscape will form where 'agent security' becomes a key differentiator for AI service providers and platforms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI