arXiv:2609.31282v1 Announce Type: new Abstract: This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle (SDLC) while also securing the agentic AI components responsible for monitoring it.
Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains
About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2609.31282
Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.
Related coverage
DistillGuard: Malicious NPM Package Detection and API Attack Chain Analysis via Static Graph and LLM Distillation
arXiv:2609.28996v1 Announce Type: new Abstract: The Node.js ecosystem heavily relies on NPM packages, and software supply chain attacks targeting malicious NPM packages…
The Target Is No Longer the Model. It’s the Agent.
AI agents are becoming the new attack surface, exposed to poisoned skills, prompt injection, jailbreaks and attacks through connected tools. I read the AI security…
SECURITY AFFAIRS MALWARE NEWSLETTER ROUND 115
Security Affairs Malware newsletter includes a collection of the best articles and research on malware in the international landscape Malware Newsletter Gray Rabbits and…
Subjects, Not Authors: The Authorship Hazard in Agentic Dataspaces
arXiv:2609.30614v1 Announce Type: new Abstract: Dataspace connectors decide whether a transfer may occur, not what the transferred value contains, tolerable for…
A Large-Scale Empirical Study of Modern Phishing Email Content
arXiv:2609.30683v1 Announce Type: new Abstract: Phishing remains one of the most pervasive threats to Internet users, and email remains its predominant delivery channel…
AGATE: Provenance-Based Runtime Defense Against Compositional Attacks on LLM Agents
arXiv:2609.30830v1 Announce Type: new Abstract: LLM agents can produce harmful effects through sequences of ordinary operations. Judging such actions requires…