arXiv:2510.06732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts. To expose this vulnerability, we present Rank Anything First (RAF), a two-stage token optimization method that crafts concise textual perturbations to consistently promote a target item in LLM-generated rankings while remaining hard to detect. Stage 1 uses Greedy Coordinate Gradient to shortlist candidate tokens at the current position by combining the gradient of the ran
The increasing integration of LLMs into critical information retrieval systems makes understanding and exploiting their vulnerabilities an immediate research priority.
This highlights a significant security and trustworthiness concern for systems relying on LLM-based ranking, impacting everything from search engines to recommendation systems.
The perceived reliability of LLMs as unbiased rankers is diminished, necessitating new methods for robustness and adversarial training.
- · Cybersecurity researchers
- · Adversarial AI development
- · Organizations developing robust retrieval systems
- · LLM-based search providers (unsecured)
- · Content creators relying on organic LLM ranking
- · Users trusting LLM rankings implicitly
Increased efforts to identify and mitigate adversarial attacks on LLM-based ranking systems will become standard.
This could lead to a 'ranking arms race' where optimizers try to manipulate rankings as fast as defenses are implemented.
The broader public perception of LLM objectivity and fairness may erode, potentially requiring regulatory oversight on AI ranking algorithms.
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Read at arXiv cs.AI