Google DeepMind

Google introduced Gemini 3.8 Flash Cyber, a cybersecurity-tuned model with more permissive cyber mitigations, available only to trusted defenders through a new Fairwind Program. Google says it prioritized vulnerability fixing over exploitation and reports 47.2% pass@1 on Collinear's CWE-Bench patching benchmark, over 70% on an internal 20-language discovery benchmark, and 2.6 times more correct Chrome patches than larger commercial models.

UK AISI's new Control Red Team tested an asynchronous reasoning monitor with Google DeepMind and successive versions of Anthropic's synchronous internal agentic-coding monitor, and reports vulnerabilities in each. Successful attacks exploited gaps in monitor prompts, weaknesses in the underlying LLM, and hard-coded rules that decide which actions reach the monitor. In a separate early experiment, an automated evolutionary search against a Claude 4.6 Sonnet monitor found attack transcripts scoring 3/100 on suspicion, versus 14-18 for best-of-N baselines. That experiment used a single-step coding task in which the agent covertly exposes its API key.

Google DeepMind introduces an AI Control Roadmap for internally deployed agents. It treats potentially misaligned AI as an insider threat, builds a threat model on MITRE ATT&CK tactics and techniques, and scales detection and response requirements with levels of agent evasion (D1-D4) and attack capability (R1-R3). It reports analyzing about a million coding agent tasks with a prototype asynchronous monitor, which informed a live monitor for its Gemini Spark agent. It says the majority of flagged events were not adversarial and often stemmed from misinterpretation or overeagerness.

Google DeepMind's Frontier Safety Framework version 3.1 introduced Tracked Capability Levels for earlier warning in CBRN and ML R&D and misalignment, and raised the recommended security for the CBRN, cyber and harmful manipulation CCLs to Security Level 2+. The cyber CCL definition itself, Cyber uplift level 1, is unchanged from v3.0.

Google DeepMind introduced CodeMender, an agent built on Gemini Deep Think models that combines static and dynamic analysis, fuzzing, differential testing and SMT solvers with LLM-based critique to generate and validate security patches. DeepMind reports 72 security fixes upstreamed to open-source projects over six months, all reviewed by human researchers before submission; in May 2026 Google said it would fold CodeMender into its enterprise agent platform.

Google DeepMind's Frontier Safety Framework version 3.0 adds a harmful manipulation CCL and expands misalignment and internal-deployment provisions. Its cyber domain keeps a single CCL, Cyber uplift level 1, for models providing sufficient uplift with high-impact cyber attacks to add expected harm at severe scale, paired with Security Level 2; the framework reasons that automated cyber-defense and social adaptation make higher security levels likely unwarranted.

Google's vice president of security announced that Big Sleep had reported 20 vulnerabilities, mostly in open-source projects such as FFmpeg and ImageMagick, with details withheld pending fixes. A Google spokesperson told TechCrunch each flaw was found and reproduced by the agent without human intervention, with a human expert reviewing reports before submission.

Google reports that, working from Google Threat Intelligence information, the Big Sleep agent found a critical SQLite memory-corruption flaw (CVE-2025-6965) that Google says was known only to threat actors and at risk of exploitation. Google says it reported the flaw for patching before attackers could exploit it, says it believes this is the first time an AI agent directly foiled an in-the-wild exploitation effort, and says Big Sleep is being applied to open-source projects.

Shi and colleagues describe Google DeepMind's continuous adaptive-attack evaluation of Gemini against indirect prompt injection in tool-use settings. On Gemini 2.0, adaptive attacks generally matched or beat non-adaptive ones against eight baseline defenses, reaching 98.4% against in-context learning and 82.4% against spotlighting, while a warning defense and a user-instruction classifier held (at most 10.8% and 3.0%). Adversarial fine-tuning for Gemini 2.5 lowered but did not eliminate attack success.

Debenedetti and colleagues (Google, Google DeepMind, ETH Zurich) propose CaMeL, which extracts control flow from the trusted user query so untrusted data cannot change which actions run, and attaches capabilities to data to block unauthorized flows. On AgentDojo the first version reported 67% of tasks solved with provable security; the June 2025 revision, with newer models, reports 77% versus 84% for an undefended system.

Google Project Zero and Google DeepMind report that Big Sleep, an LLM agent evolved from Project Naptime, found an exploitable stack buffer underflow in SQLite during a variant-analysis experiment seeded with newly landed commits. The bug was reported in early October 2024 and fixed the same day, before it appeared in an official release; Google believes this was the first public example of an AI agent finding such a memory-safety flaw in widely used software.