负责任且安全地开发 Gemini 4 Argon

Learn more about our approach to safety and security for Gemini 4 Argon. Head of Gemini Security, Google DeepMind Gemini 4 Argon is a step forward in what models can do — especially when it comes to deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science. Its cyberdefense capabilities are frontier-level and designed to give defenders an advantage over attackers. To maximize its potential, we first wanted cyber defenders to put it to work via our Fairwind Program , while our own security teams used it to scan critical systems inside and outside of Google. This window of limited access showed why these capabilities matter. For instance, Wiz used Argon to uncover a critical bug in hospital software that previous models had missed, helping protect hundreds of thousands of patient records. Meanwhile, Google’s own security teams used Argon to find and patch bugs across internal software or open-source software. This included fixing foundational vulnerabilities affecting systems globally, which are going through our responsible disclosure process. Today, we are beginning to roll out Argon broadly , as well as expanding our Fairwind Program to additional trusted cyber defenders. To do this well, we grappled with two big questions: How do we build the right safety guardrails? If Argon can help defenders find complex software bugs or help scientists design experiments, how do we stop bad actors from using it to launch cyberattacks or create biological threats? ( Misuse ) Can we trust the model to do the job the right way? As models become more resourceful problem-solvers, how do we make sure they don’t cut corners, cross boundaries, or make incorrect high-stakes decisions? ( Misalignment and Prompt Injection ) Our full Safety and Security Report systematically covers our set of defenses. Below are some highlights from how we approached these two questions for Gemini 4 Argon. The same coding and reasoning improvements that enabled Argon to find and patch vulnerabilities also drove better performance on our cybersecurity tests — solving all 12 of the end-to-end simulated cyberattack scenarios, up from five in our previous generation. In biology evaluations, unguarded versions of the model also showed a much stronger ability to help troubleshoot complex lab procedures step by step. Following our Frontier Safety Framework , since we saw elevated capabilities in the cyber and biological domains, we put our next-generation set of frontier safeguards in place across both areas. First, when it comes to potentially dangerous requests — from writing exploit code to helping with dangerous biological materials, Argon is significantly better at not fulfilling harmful requests than our previous models. On an adversarial set meant to stress test the model, violations reduced nearly twofold — also significantly better than previous Gemini models. Second, we wrap the model in three layers of real-time protection; this is to prevent determined attackers that might try to jailbreak (i.e. trick) the model by disguising a dangerous request as a harmless story or breaking it into dozens of small questions over several weeks. Here is how these protections work: Input screening: Smaller models check incoming requests and conversation context for malicious intent or dangerous requests. Internal introspection: Even if a request is cleverly disguised to look harmless, small transformers ("activation probes") read the model’s internal neural signals and are trained to spot when it is activating knowledge about cyberattacks or biological threats.