arXiv Brief — 2026-10-01
New work on securing agentic LLMs via kernel-level enforcement and cryptographic provenance, alongside advances in emulation-based dynamic analysis.
New work on securing agentic LLMs via kernel-level enforcement and cryptographic provenance, alongside advances in emulation-based dynamic analysis.
Agent control boundaries under stress: vault-mediated credentials, spoofed subagent identities, and backchain memory attacks; plus traffic analysis and privacy-preserving cloud consultation.
LLM assistance in reverse engineering, side-channel device tracking in exclusive-use IoT, and defensive measures for differentially private streams and iteratively pruned deep neural networks.
A batch of new research from NDSS exploring pitfalls in LLM security evaluations, automated insider threat simulation, distributed SNARKs, and more.
Infostealer data-driven analysis, LLM honeypots, persistent billable state DoW, bypassing unlearning via tokenization, DistillGuard for NPM, MoSign VR authentication, AgentKernel native trust, and Byzantine-robust intrusion detection.