Context Engineering | Compaction & Agent Memory for Automated Malware Analysis
Compaction cut input tokens 86% across long-running agent evals with no quality loss. Context discipline matters as much as model selection.
Compaction cut input tokens 86% across long-running agent evals with no quality loss. Context discipline matters as much as model selection.
DPRK-linked implant embeds 38 fabricated system messages that spoof an LLM triage harness, hiding a credential stealer and Telegram C2 underneath.
Decades of piling complexity onto non-standardized stacks have left security unsteerable. Juan Andrés Guerrero-Saade makes the case for a new approach.
ESET researchers show how Gamaredon facilitated Turla access to Ukrainian targets, revealing rare cooperation between FSB-linked espionage groups.
Mick Baccio and Scott Roberts examine whether public breach signals and market timing models can turn cyber incidents into actionable trading opportunities.
Cloud attack framework skips cryptomining, harvests financial, messaging, and enterprise credentials for fraud, spam, and potential extortion.
Joe FitzPatrick reveals how consumer imports of networked devices pose a real security risk to small businesses and critical infrastructure alike.
A previously unknown 2005 cyber sabotage framework patches high-precision calculation software in memory to silently corrupt results.
Marc Rogers and Silas Cutler expose how cheap smart home devices conceal a shadow supply chain of shell companies, firmware flaws, and foreign data routing.
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.