The security intelligence platform for AI teams
AI security threats move fast and get buried under hype and noise. Built by an Information Systems Security researcher to help security teams and developers stay ahead of vulnerabilities, privacy incidents, safety research, and policy developments.
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Neural Network Robustness Testing Methods Surveyed: An academic review catalogs techniques for assessing whether image recognition systems maintain accuracy when confronted with adversarial inputs (deliberately crafted inputs designed to fool AI models) or unexpected conditions.
Generative AI Reshapes Ransomware Defense Calculus: Analysis argues that conventional defenses against ransomware (malicious software that encrypts files and demands payment) may prove inadequate as generative AI tools enable more sophisticated attacks and alter the threat landscape.
This article discusses red teaming techniques (testing methods where security professionals act as attackers to find weaknesses) that organizations can use to identify privacy issues in their systems and infrastructure. The author emphasizes that privacy violations often come from insider threats (employees or contractors with authorized access to sensitive data), and highlights the importance of regular privacy testing as required by regulations like GDPR (General Data Protection Regulation, which sets rules for protecting personal data in Europe). The article mentions the "Motivated Intruder" threat model, where an insider with access to anonymized datasets (data with identifying information supposedly removed) uses data science techniques to reidentify people and expose their identities.