arXiv:2606.13610v1 Announce Type: new Abstract: Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: generative recommenders may consume polluted web content, such as fake reviews and promotional pages crafted to mislead recommendations. We ask: to what extent do search-augmented LLMs become unwitting promoters of fake products when consuming polluted retrieval results? To answer this, we introduce FORGE (Fake Online Recommendations in Generative Environments), a benchmark for measuring fake-product promotion unde
The proliferation of search-augmented LLMs and generative AI for recommendations makes the issue of content pollution an immediate and pressing concern.
This research highlights a critical vulnerability in AI-driven recommendation systems, demonstrating how easily they can be manipulated to promote fake or misleading products, eroding trust and impacting consumer behavior.
The understanding of AI recommenders shifts from purely beneficial to potentially compromised, necessitating robust defenses against content pollution and adversarial attacks.
- · AI security researchers
- · Content verification platforms
- · Ethical AI developers
- · E-commerce platforms with uncurated AI recommendations
- · Consumers relying solely on AI recommendations
- · Generative AI models without robust filtering
Generative recommenders will frequently promote fake or misleading products if not adequately secured against polluted web content.
Public trust in AI-driven recommendations will decline, leading to increased skepticism and a demand for transparency and auditability.
New regulations specifically targeting AI content integrity and recommender system accountability may emerge to protect consumers from algorithmic manipulation.
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Read at arXiv cs.CL