Attack library

Ten ways AI agents get talked out of money.

People get refunds, discounts, settlements, better prices and better terms out of AI agents with ten families of tactics, from fake manager approval and invented policies to instructions hidden in a ticket. Each family below explains how the attack works, why agents fall for it and the controls that stop it.

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CodeFamilyWhat the attacker doesTechniques
T1Negotiation pressureAnchors, fake rival quotes, false deadlines and nibbling that push an agent past its price or terms floor.Extreme anchor · Fake competing offer · False deadline and walk-away bluff · Reciprocal concession ladder · Nibbling and stacking
T2Social engineeringFake approvals, staff impersonation, misquoted policy, sympathy and threats that talk an agent into paying.Fake prior approval · Staff or executive impersonation · Policy misquote · Sympathy and urgency · Polite threats
T3Multi-turn escalationSmall, innocent-looking steps across one chat that add up to a concession no single message would get.Crescendo · Commit then escalate · Request splitting · Rule rewriting · Rephrase after refusal
T4Indirect prompt injectionInstructions hidden in tickets, attachments, web pages or tool output that the agent obeys as yours.Hidden text in attachments · Poisoned product page · Pasted “evidence” · Fake system or internal note · Tool-output poisoning
T5Information extractionProbing for an agent’s floor, approval limits and private rules so the attacker can ask for the maximum.Hypothetical threshold questions · Concession probing · Reason menu · Social comparison · Instruction extraction
T6Human fraud patternsItem-not-received claims, wardrobing, empty boxes, edited evidence and repeat claims aimed at AI agents.Item not received · Wardrobing and self-inflicted damage · Empty box and weight mismatch · Edited evidence · Refund-as-a-service
T7Mandate and identity abuseMismatched or replayed mandates and impersonated owners that get an agent to pay or change an account.Mandate mismatch · Claimed approval · Approval replay · Owner impersonation · Advance-fee deal
T8Memory and multi-session attacksFalse history planted in agent memory or notes, or trust built over sessions, then used for a large ask.Note planting · False history · Long con · Account cycling · Shared memory poisoning
T9Multi-agent collusionCoordinated accounts or agents, and pricing agents reacting to each other, that beat limits set per party.Split claims · Probe then hand off · Manipulative seller agents · Tacit price coordination · Feedback loops
T10Stalling and denialEndless haggling, loops and tool-budget exhaustion that push an agent into conceding or an unsafe fallback.Attrition · Loop induction · Malformed confirmations · Timeout baiting · Promises instead of payment

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