arXiv:2605.22438v1 Announce Type: cross Abstract: Shilling is the use of artificial bids to make competition appear stronger and push prices upward. We study repeated first-price auctions in which shilling affects feedback but not allocation: the learner wins or loses against the real competing bid, but after a loss observes the maximum of the real bid and an independent shill bid. Thus the manipulation changes what the learner observes and hence how it learns to bid, without changing the outcome of the current auction. We analyze regret with respect to the best bid benchmark, assuming that th
The proliferation of AI systems in economic contexts, including bidding and trading, makes understanding the mechanisms and vulnerabilities of automated interactions increasingly critical.
This research highlights a fundamental challenge for AI agents operating in adversarial environments, where feedback loops can be intentionally manipulated to extract greater value.
The focus on feedback manipulation, rather than direct outcome manipulation, introduces a new vector for strategic attacks in automated marketplaces that AI agents must learn to counter.
- · AI algorithm developers
- · Auction platform security teams
- · Researchers in game theory and AI safety
- · Bidders with sophisticated AI agents
- · Naive AI bidding agents
- · Less sophisticated auction platforms
- · Unsuspecting buyers in online auctions
AI agents will require more robust models of adversary behavior and feedback mechanisms to operate effectively in competitive environments.
The development of 'trustless' or verifiable feedback systems may accelerate in various online marketplaces to combat such manipulation.
This could lead to an arms race in AI-driven market manipulation and counter-manipulation, impacting efficiency and fairness in digital economies.
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