Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents

2026-03-12T07:23:32Z5fe737080c371dc41e6b246c651c519e8f77709d1c0b8d548260cb3933fbc917
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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