{"data":{"id":"77561f76-d419-4a3f-8ac9-bc362a27e933","title":"Chatter Attack: Resource Consumption Attack for Large Language Models","summary":"Chatter Attack is a resource consumption attack on large language models that optimizes adversarial prompts to induce short self-doubt target phrases, which sustains long generations without the search-space explosion of earlier methods. Using inference attention loss and global entropy loss, it raises maximum response length up to 31.5x across multiple models and datasets. For black-box settings, the authors add Bayesian Prompt Optimization (BPO), which builds a discrete token kernel to explore the search space.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2611.11261v1","publishedAt":"2026-11-08T09:26:00.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"low","attackType":["denial_of_service"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-11-08T09:26:00.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["availability"],"aiComponentTargeted":"inference","llmSpecific":true,"classifierConfidence":0.95,"researchCategory":"preprint","atlasIds":null}}