Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
arXiv:2606.13385v1 Announce Type: cross Abstract: Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences. This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour. Existing security benchmarks adopt an \textit{attack-centric} perspective, focusing on the technical feasibility of injections while overlooking the nuanced distribution of resulting harms. In practi
As LLM-driven web agents move into real-world deployments, the immediate and tangible risks of prompt injection attacks are becoming critical concerns, leading to a focus on robust security frameworks.
This research highlights a significant vulnerability for autonomous AI systems, which could undermine trust, financial stability, and operational integrity for organizations deploying them.
The focus shifts from merely technical feasibility of prompt injection to a stakeholder-centric view, necessitating new security benchmarks and development practices prioritizing harm distribution and mitigation.
- · AI security firms
- · Auditors and compliance experts
- · Developers of robust web agent platforms
- · Companies deploying insecure web agents
- · Users vulnerable to manipulated agent actions
- · Bad actors relying on simple prompt injection
Increased investment in AI security protocols and prompt injection defenses for web agents.
New regulatory frameworks specifically addressing the security and accountability of autonomous AI agents operating online.
The emergence of 'AI red teaming' as a critical industry, specializing in identifying and mitigating AI-specific vulnerabilities before deployment.
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Read at arXiv cs.AI