How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation
arXiv:2606.16821v1 Announce Type: new Abstract: Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that attacker-published pages are transformed into endorsed claims. We introduce SearchGEO, a controlled evaluation framework for measuring endorsement corruption in LLM-based web-search agents, combining a web-evidence manipulation pipeline, a five-mode attack taxonomy, and multiple output-level metrics. We evaluate 13 LLM backends on 308 cases each. Results show that vulnerability patterns vary across ba
The proliferation of LLM-based search agents necessitates immediate assessment of their vulnerabilities as they are increasingly deployed for user recommendations.
This research highlights critical security flaws in LLM search agents, posing significant risks to information integrity and user trust in AI-generated content.
The understanding of how easily LLM search agents can be manipulated will drive new security protocols and evaluation frameworks for their development and deployment.
- · Cybersecurity researchers
- · AI ethics and safety organizations
- · Developers of robust LLM security tools
- · Users trusting unverified LLM recommendations
- · Companies deploying insecure LLM agents
- · LLM developers ignoring security vulnerabilities
Increased scrutiny and demand for 'secure by design' principles in LLM development.
Development of new attack vectors and defenses, creating an arms race in LLM security.
Potential for regulatory bodies to mandate specific security standards for AI agents, impacting their commercialization.
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Read at arXiv cs.CL