{"data":{"id":"2902289c-0189-4006-9f17-4400d028f9dc","title":"Text Adversarial Attacks With Dynamic Outputs","summary":"This research describes a new attack method called TDOA (Textual Dynamic Outputs Attack) that can trick large language models by exploiting a weakness in how they handle variable outputs. Unlike older attack methods that assume a fixed set of possible answers, real-world LLMs often generate answers that go beyond predefined categories or produce different numbers of labels depending on the input, creating what researchers call 'dynamic outputs.' TDOA works by using a clustering approach (grouping similar outputs together) to convert these unpredictable outputs into a simpler form that existing attack techniques can target, achieving up to 80.8% success rates with very few queries.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11653428","publishedAt":"2026-08-12T13:16:39.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":["OpenAI"],"affectedVendorsRaw":["GPT-4o","GPT-4.1","OpenAI"],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-08-12T13:16:39.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"inference","llmSpecific":true,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}