BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models
arXiv 2605.12529•37ad203f0d64821522920c4755df14b92512812d3e93f9c0b038795a7ee7766f
CPythonCoT-monitoringLLM-backdoorsLuaQuickJSRoPESafeContextautonomous-drivingbackdoor-detectioncontext-assemblydecision-time-assemblyhard-brakinghidden-objectivesjailbreaksmodel-extraction-benchmarks','GNN-extraction','watermarking-defemodel-unlearningoverride-hookspersona-conditioned-attacksphysical-adversarial-camouflagered-teamingscript-runtimessemantic-fuzzingsmall-model-monitoringtrajectory-manipulationwatermark-preservation
Paper metadata
- arXiv ID
- 2605.12529
- Version
- Not specified by this published record
- Category
- Computer Science — Cryptography and Security (cs.CR)
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Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- 37ad203f0d64821522920c4755df14b92512812d3e93f9c0b038795a7ee7766f
- Enrichment time
- 2026-05-14T07:23:35Z
- AI-assisted enrichment
- Yes
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