Google DeepMind independently reports adaptive attacks above 90% on Gemini.
Week of May 19–25, 2025
What changed in what we know
Google DeepMind finds adaptive attacks exceed 90% success against spotlighting-style defenses on Gemini.
New findings
Attacks & incidents
Legit Security reports that hidden instructions in merge requests, comments or code could steer GitLab Duo, combined with unsanitized HTML in streamed responses, to leak private project code and confidential issues. GitLab was notified on 2025-02-12 and patched rendering of external-domain HTML tags.
Capability & gating
Anthropic activated ASL-3 protections for Claude Opus 4 as a precaution because it could not rule out ASL-3 CBRN risk; the announcement does not cite cyber capability as the trigger. The ASL-3 security standard it describes includes more than 100 controls to protect weights, two-party authorization for weight access, and egress bandwidth controls against exfiltration.
Defense & research
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.
BountyBench, from Stanford-led researchers, builds 40 bug bounties across 25 real-world systems into 120 Detect, Exploit and Patch tasks with dollar values attached. In the first version the best Detect score was 5%, while OpenAI Codex CLI and Claude Code scored 90% and 87.5% on Patch, well above their Exploit scores. A July 2025 revision with more agents reported Codex CLI with o3-high at 12.5% on Detect and 90% on Patch.
Policy & standards
The NSA AI Security Center, CISA, the FBI and international partners released a cybersecurity information sheet on securing data used to train and operate AI systems across the lifecycle. It recommends robust data protection, proactive risk management and stronger monitoring and threat detection, and is aimed at defense industrial base, national security system, federal and critical infrastructure operators.