Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers
arXiv 2607.07739•cd6f56b82056a48d8c94807cb60d7b403a8c207a59da8d71d4fcbdb024c04094
KS-CFARISC-VScopeJudgeTEEadversarial-mlagent-monitoringcontrol-flow-attestationcontrollability-aware-attacksdataset-releaseforensic-schemaforensicsidsjailbreaksllm-securitymechanistic-interpretabilitynetwork-intrusion-detectionoffensive-securitypre-execution-gatingpsychological-manipulationscope-enforcementsocial-engineeringsymbolic-replayt2i-safetytext-to-imagetransfer-attacks
Paper metadata
- arXiv ID
- 2607.07739
- 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
- cd6f56b82056a48d8c94807cb60d7b403a8c207a59da8d71d4fcbdb024c04094
- Enrichment time
- 2026-07-10T07:23:34Z
- AI-assisted enrichment
- Yes
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