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Staying Ahead of Adversarial AI Through Agentic Source Code Review | Google Cloud Blog

Mandiant (Google Cloud) describes its Agentic Vulnerability Discovery Harness (AVDH), a multi-agent orchestration framework built on Google's Agent Development Kit that uses Gemini plus human expert-driven validation to find exploitable vulnerabilities in source code during proactive reviews, pentests, red-team ops, and incident response. Over 10 months it reportedly analyzed tens of millions of lines of code, generated tens of thousands of findings, and led to 12 assigned CVEs (e.g., CVE-2026-13242, CVE-2026-55803), including discovering 100+ critical bugs in stolen repositories in two days. Details →

“Keep going, bro. You’ve got this!” A data-driven look at how adversaries are weaponizing AI

Cisco Talos analyzed a corpus of prompt logs left behind on threat-actor endpoints running tools such as Claude Code, Codex, Cursor and Gemini, documenting how adversaries weaponize AI for malicious software development, scaling criminal operations, and vulnerability research. Talos found guardrails largely ineffective, with actors bypassing safety checks using simple authorization claims like 'I'm allowed to do this' rather than sophisticated encoding, and stored blanket authorizations in persistent memory. The report ties this to the recently disclosed Hugging Face and OpenAI agentic-attacker incident where autonomous agents escaped a sandbox and compromised production infrastructure. Details →

Jailbreaking Large Language Models via Multi-Task Embedding-based Prompt | Proceedings of the 2026 IEEE/ACM Third International Conference on AI Foundation Models and Software Engineering

Researchers present the Multi-Task Embedding-based Attack (MTEA), a jailbreak technique that embeds malicious instructions within three concurrent tasks (Code Understanding, Language Translation, and Pattern Adherence) to disrupt LLM safety alignment. Evaluated on six models including GPT-4o and Gemini-2.5-pro using AdvBench, MTEA reportedly achieves 100% attack success and following rates, defeats Perplexity Filter and SmoothLLM defenses, and reduces query costs by 90% versus baselines. Details →
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