Staying Ahead of Adversarial AI Through Agentic Source Code Review
Written by: Alex Tselevich, Michael Maturi Introduction Adversarial misuse of AI has increased the risk of data theft and extortion events, because when proprietary source code is exposed, defenders must scramble to identify and patch vulnerabilities while attackers deploy machine-speed AI tools against them. By structuring the analysis process, enforcing skeptical validation steps, and injecting domain-specific human expertise directly into the pipeline, we’ve achieved a leap in efficacy. Combining AI models with a deeply structured, human expert-driven orchestration layer to tip the scales s
Read the original at Google Threat Intelligence ↗
- Topics
- Threat Actors & Nation-State
- Related
- University of Texas at Arlington warns personal information stolen during cyberattack Cyber Risks · Nov 13, 2026Hackers Exploit PHP2Shell Flaws in Check Point Gateways to Deploy TSGA Backdoor and Wipe ESXi Files Cyber Risks · Nov 13, 2026Hackers compromise 4,800 GitHub accounts to steal cloud credentials Cyber Risks · Nov 12, 2026