Building an Adversarial Consensus Engine | Multi-Agent LLMs for Automated Malware Analysis
Single-tool LLM analysis produces reports that look authoritative but aren't. A serial consensus pipeline catches artifacts and hallucinations at source.
Single-tool LLM analysis produces reports that look authoritative but aren't. A serial consensus pipeline catches artifacts and hallucinations at source.
Andrew MacPherson exposes how crypto thieves exploit DeFi architecture, from the $1.5 billion Bybit heist to drainers-as-a-service and fund laundering.
LLMs can turn CTI narratives into structured intelligence at scale, but speed-accuracy trade-offs demand careful design for operational defense workflows.
Analysis of 175,000 open-source AI hosts across 130 countries reveals a vast compute layer susceptible to resource hijacking and code execution attacks.
Dan Tentler reveals how consumer hardware coupled with Home Assistant can monitor hotel rooms, detect occupants through walls, and trigger automated alerts.
LLM cybersecurity benchmarks fail to measure what defenders need: faster detection, reduced containment time, and better decisions under pressure.
Jim Walter unpacks the hacktivist landscape and reveals how to distinguish different levels of threat based on persona characteristics.
Learn how attackers exploit tokenization, embeddings and LLM attention mechanisms to bypass LLM security filters and hijack model behavior.
LLMs make competent ransomware crews faster and novices more dangerous. The risk is not superintelligent malware, but rather industrialized extortion.
Read how two Cisco Network Academy Cup winners went from students to operators behind Salt Typhoon, a global cyber espionage campaign targeting telecoms.
Mei Danowski & Eugenio Benincasa unpack how Chinese firms running attack-defense exercises fuel state-linked offensive cyber operations.