<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:media="http://search.yahoo.com/mrss/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:webfeeds="http://webfeeds.org/rss/1.0"><channel><title>Elie on AI and Cybersecurity</title><description>AI and Cybersecurity research, talk and blog in-depth articles</description><link>https://elie.net</link><atom:link href="https://feeds.feedburner.com/inftoint" rel="self" type="application/rss+xml"/><webfeeds:cover image="https://elie.net/category-facebook.png"/><webfeeds:icon>https://elie.net/favicon/favicon.svg</webfeeds:icon><webfeeds:logo>https://elie.net/favicon/favicon.svg</webfeeds:logo><webfeeds:accentColor>0099ff</webfeeds:accentColor><webfeeds:related layout="card" target="browser"/><item><title>Facade: High-Precision Insider Threat Detection Using Deep Contextual Anomaly Detection</title><link>https://elie.net/publication/facade-high-precision-insider-threat-detection-using-deep-contextual-anomaly-detection</link><guid isPermaLink="true">https://elie.net/publication/facade-high-precision-insider-threat-detection-using-deep-contextual-anomaly-detection</guid><description>Facade detects insider threats by learning the context of everyday activity, achieving the precision needed to protect a large corporate environment.</description><pubDate>Wed, 12 Aug 2026 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/facade-high-precision-insider-threat-detection-using-deep-contextual-anomaly-detection.CF6Y12fg.jpg"/></item><item><title>ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?</title><link>https://elie.net/publication/exploitgym-can-ai-agents-turn-security-vulnerabilities-into-real-attacks</link><guid isPermaLink="true">https://elie.net/publication/exploitgym-can-ai-agents-turn-security-vulnerabilities-into-real-attacks</guid><description>ExploitGym measures whether AI agents can turn real vulnerabilities into working exploits across applications, V8 and the Linux kernel.</description><pubDate>Mon, 11 May 2026 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/exploitgym-can-ai-agents-turn-security-vulnerabilities-into-real-attacks.By1Y81xR.jpg"/></item><item><title>DROIDCCT: Cryptographic Compliance Test via Trillion-Scale Measurement</title><link>https://elie.net/publication/droidcct-cryptographic-compliance-test-via-trillion-scale-measurement</link><guid isPermaLink="true">https://elie.net/publication/droidcct-cryptographic-compliance-test-via-trillion-scale-measurement</guid><description>DroidCCT tests Android cryptography at ecosystem scale, analyzing trillions of samples from half a billion devices to identify implementation weaknesses.</description><pubDate>Mon, 08 Dec 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/droidcct-cryptographic-compliance-test-via-trillion-scale-measurement.BNKhn1Ni.jpg"/></item><item><title>Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks</title><link>https://elie.net/publication/evaluating-the-robustness-of-a-production-malware-detection-system-to-transferable-adversarial-attacks</link><guid isPermaLink="true">https://elie.net/publication/evaluating-the-robustness-of-a-production-malware-detection-system-to-transferable-adversarial-attacks</guid><description>A study of Gmail malware detection shows how adversarial attacks on one ML component affect the entire pipeline, and evaluates a deployed defense.</description><pubDate>Wed, 19 Nov 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/evaluating-the-robustness-of-a-production-malware-detection-system-to-transferable-adversarial-attacks.ZH_wFgO3.jpg"/></item><item><title>Integrating Large Language Models into Security Incident Response</title><link>https://elie.net/publication/integrating-large-language-models-into-security-incident-response</link><guid isPermaLink="true">https://elie.net/publication/integrating-large-language-models-into-security-incident-response</guid><description>A study with security analysts examines how large language models can help summarize incidents, where they make mistakes and when collaboration helps.</description><pubDate>Mon, 11 Aug 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/integrating-large-language-models-into-security-incident-response.KpsD8piB.jpg"/></item><item><title> FACADE High-Precision Insider Threat Detection Using Contrastive Learning</title><link>https://elie.net/talk/facade-high-precision-insider-threat-detection-using-contrastive-learning</link><guid isPermaLink="true">https://elie.net/talk/facade-high-precision-insider-threat-detection-using-contrastive-learning</guid><description>This talk presents FACADE, a novel self-supervised AI system used by Google to detect insider threats with high precision. FACADE uses a contrastive learning strategy trained solely on benign data, achieving a false positive rate below 0.01%.</description><pubDate>Thu, 07 Aug 2025 09:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/facade-high-precision-insider-threat-detection-using-contrastive-learning.BoMtNQBH.jpg"/></item><item><title>Autonomous Timeline Analysis and Threat Hunting</title><link>https://elie.net/talk/autonomous-timeline-analysis-and-threat-hunting-an-ai-agent-for-timesketch</link><guid isPermaLink="true">https://elie.net/talk/autonomous-timeline-analysis-and-threat-hunting-an-ai-agent-for-timesketch</guid><description>Autonomous timeline analysis and threat hunting with the Sec-Gemini digital forensic agent, and the Timesketch AI Panel designed to make its findings actionable.</description><pubDate>Wed, 06 Aug 2025 09:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/timeline-analysis-in-timesketch-with-sec-gemini.BRltR0ME.jpg"/></item><item><title>Supporting the Digital Safety of At-Risk Users: Lessons Learned from 9+ Years of Research and Training</title><link>https://elie.net/publication/supporting-the-digital-safety-of-at-risk-users-lessons-learned-from-9-years-of-research-and-training</link><guid isPermaLink="true">https://elie.net/publication/supporting-the-digital-safety-of-at-risk-users-lessons-learned-from-9-years-of-research-and-training</guid><description>Lessons from more than nine years of research and training at Google inform how to support the digital safety of people facing elevated risks.</description><pubDate>Sat, 14 Jun 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/supporting-the-digital-safety-of-at-risk-users-lessons-learned-from-9-years-of-research-and-training.C8m6hOUQ.jpg"/></item><item><title>Supporting Human Raters with the Detection of Harmful Content Using Large Language Models</title><link>https://elie.net/publication/supporting-human-raters-with-the-detection-of-harmful-content-using-large-language-models</link><guid isPermaLink="true">https://elie.net/publication/supporting-human-raters-with-the-detection-of-harmful-content-using-large-language-models</guid><description>Five design patterns show how large language models can assist human content reviewers, improving capacity and the detection of harmful material.</description><pubDate>Mon, 12 May 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/supporting-human-raters-with-the-detection-of-harmful-content-using-large-language-models.CilYUyiS.jpg"/></item><item><title>Magika: AI-Powered Content-Type Detection</title><link>https://elie.net/publication/magika-ai-powered-content-type-detection</link><guid isPermaLink="true">https://elie.net/publication/magika-ai-powered-content-type-detection</guid><description>Magika uses a compact neural model to identify file content types accurately and quickly, supporting attachment scanning and malware analysis.</description><pubDate>Sat, 26 Apr 2025 12:00:00 GMT</pubDate><media:content type="image/jpeg" width="1774" height="887" medium="image" url="https://elie.net//_astro/magika-ai-powered-content-type-detection.LyfvgKYB.jpg"/></item></channel></rss>