Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents
2026-03-12T07:23:32Z•5fe737080c371dc41e6b246c651c519e8f77709d1c0b8d548260cb3933fbc917
AIBOMLLM-securityOAuthSBOMactivation-steeringadversarial-MLagent-securityautomated-program-repairbackdoor-defensebit-flip-attackdata-overaccessfederated-learningfederated-learning-securitygovernancehallucination-detectionhardware-fault-injectionjailbreakingprovenancered-teamingsoftware-supply-chaintool-verification
What happened
Collection of March 12, 2026 security-relevant arXiv papers covering attacks, defenses, and supply-chain/agent governance for modern AI and distributed systems. Key findings: Flip-Agent demonstrates the first targeted bit-flip attack (BFA) framework against multi-stage LLM-based agents, enabling manipulation of outputs and tool invocations and exposing a critical hardware/fault-injection attack surface for agent pipelines. NabaOS proposes a practical, low-latency HMAC-signed tool-receipt scheme to detect agent hallucinations in real time as an alternative to expensive zk-proof approaches. SBOM
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- 5fe737080c371dc41e6b246c651c519e8f77709d1c0b8d548260cb3933fbc917
- Enrichment time
- 2026-03-12T07:23:32Z
- AI-assisted enrichment
- Yes
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