IndirectAD: Practical Data Poisoning Attacks Against Recommender Systems for Item Promotion

Summary

Researchers introduce IndirectAD, a data poisoning attack against recommender systems inspired by Trojan attacks on machine learning. The attack first promotes a trigger item, then transfers that advantage to a target item by creating co-occurrence data between them, which reduces the number of controlled accounts needed. Experiments on multiple datasets and recommender systems show noticeable impact with only 0.05% of a platform's user base.

Topics

• Adversarial machine learning

Related items

• CriticalCVE-2026-108263: Astron Agent code-node execution as root through workflow run endpointsSimilar attack · NVD/CVE Database
• MediumHackers abuse Google Ads, Bing redirects to push Claude ClickFix attacksSimilar attack · BleepingComputer
• CriticalHermes Agent - PKCE Session Takeover via Redirect-URI Parser ConfusionSimilar attack · Tenable Research Advisories
• LowSocial Engineering AI Agents: The New BEC for 2026Similar attack · Dark Reading
• HighGHSA-cv3g-hj65-pcfh: PraisonAI: Shell command allowlist bypass via find -exec built-in actionSimilar attack · GitHub Advisory Database