{"data":{"id":"52e2eb44-a404-41ef-9051-c70282ba44f0","title":"Rethinking Neural Decompilation: Candidate Selection as the Hidden Bottleneck","summary":"This paper studies why neural decompilers based on LLM4Decompile fall short of their best-sample accuracy on obfuscated binaries. It shows correct reconstructions are usually already among the model's samples, reaching 93% pass@100 on HumanEval-Decompile. A generate-then-select pipeline using negative perplexity and execution-guided selection, plus one epoch of fine-tuning on the released ExeBench-Obfuscated dataset (362,647 OLLVM samples), raises pass@1 on obfuscated binaries to 88.8%.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"https://arxiv.org/abs/2611.02788v1","publishedAt":"2026-11-02T09:02:54.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["LLM4Decompile","Ghidra","IDA Pro"],"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":"2026-11-02T09:02:54.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.8,"researchCategory":"preprint","atlasIds":null}}