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.
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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.
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From Narrative to Knowledge Graph | LLM-Driven Information Extraction in Cyber Threat Intelligence
LLMs can turn CTI narratives into structured intelligence at scale, but speed-accuracy trade-offs demand careful design for operational defense workflows.
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Silent Brothers | Ollama Hosts Form Anonymous AI Network Beyond Platform Guardrails
Analysis of 175,000 open-source AI hosts across 130 countries reveals a vast compute layer susceptible to resource hijacking and code execution attacks.
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LLMs in the SOC (Part 1) | Why Benchmarks Fail Security Operations Teams
LLM cybersecurity benchmarks fail to measure what defenders need: faster detection, reduced containment time, and better decisions under pressure.
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Inside the LLM | Understanding AI & the Mechanics of Modern Attacks
Learn how attackers exploit tokenization, embeddings and LLM attention mechanisms to bypass LLM security filters and hijack model behavior.
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LLMs & Ransomware | An Operational Accelerator, Not a Revolution
LLMs make competent ransomware crews faster and novices more dangerous. The risk is not superintelligent malware, but rather industrialized extortion.
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Prompts as Code & Embedded Keys | The Hunt for LLM-Enabled Malware
LLM-enabled malware poses new challenges for detection. SentinelLABS presents groundbreaking research on how to hunt for this new class of threats.
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