{"data":{"id":"c80cf2cf-360f-4693-9fa7-ebbf1229140e","title":"Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompt Attacks","summary":"Researchers propose robustness of prompting (RoP), a prompting strategy meant to make large language models less sensitive to input perturbations such as typographical errors and slight character order errors. RoP has two stages: Error Correction, which generates adversarial examples and prompts that fix input errors automatically, and Guidance, which builds an optimal guidance prompt from the corrected input. Experiments on arithmetic, commonsense, and logical reasoning tasks show RoP significantly improves robustness against adversarial perturbations with only minimal accuracy degradation compared to clean input.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11535755","publishedAt":"2026-05-27T13:22:19.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":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":"2026-05-27T13:22:19.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}