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February 2025 • Model Security & Safety Evaluation
Want to see how Gemini 2.0 Flash stacks up against other models? Use our comparison tool to analyze security metrics side by side.
Google's Gemini 2.0 Flash launched in February 2025, introducing significant improvements in processing speed and context handling. Following its general availability announcement across Google AI Studio and Vertex AI, we conducted a comprehensive security evaluation to understand its safety measures and potential vulnerabilities.
As enterprise adoption increases, this analysis examines both the model's security features and areas for potential improvement.
"2.0 Flash delivers significant performance improvements across key benchmarks, with expanded capabilities including image generation and text-to-speech in development."— Google Announcement, February 2025
Text, Image, Video, Audio processing, Multimodal capabilities
Text generation
1M input tokens input • 8k output tokens output
Low latency scenarios
• Search as a tool • Code execution • Automating tasks
• Google AI Studio • Gemini API • Vertex AI • Gemini App
June 2024
Comprehensive analysis across 39 test categories, sorted by severity and pass rate:
Our security testing suite demonstrates varying levels of compliance across different categories. The Gemini 2.0 Flash model's overall pass rate distribution shows 3 critical, 5 high, 15 medium, and 16 low severity findings.
Top performing areas include ASCII Smuggling (100%), Unauthorized Commitments (82.22%), Divergent Repetition (77.78%). These categories show satisfactory security practices.
Areas requiring attention include Pliny Prompt Injections (0%), Disinformation Campaigns (17.78%), Overreliance (20%). These categories present opportunities for improvement in security measures.
Tests for unauthorized contractual commitments and legal exposure
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Tests for fabrication of false or misleading information
Our evaluation included over 1,600 test probes across four security categories. The analysis revealed both strengths and areas requiring additional safety measures:
Protection against unauthorized access, data exposure, and system vulnerabilities
Assessment of compliance with legal requirements and prevention of illegal content
Prevention of harmful content and protection of user safety
Protection of brand integrity and prevention of misuse
OWASP Top 10 for LLMs (2025) and MITRE ATLAS represent the primary security assessment frameworks for large language models. OWASP focuses on critical deployment risks, while ATLAS maps adversarial techniques against AI systems, similar to the MITRE ATT&CK framework for traditional systems.
The Gemini 2.0 Flash model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While Gemini 2.0 Flash has no critical vulnerabilities, high-severity issues should be prioritized for improvement.
High-severity concerns include LLM02: Sensitive Information Disclosure.
MITRE ATLAS identified Prompt Injections and Jailbreak as high-severity concerns.
39
48.3%
3
Tests handling of Pliny prompt injections
Tests handling of false information campaigns
Tests for overreliance on system assumptions
Tests for unauthorized impersonation of entities
Tests handling of general illegal activities
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Tests for unauthorized contractual commitments and legal exposure
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Tests for unauthorized actions beyond defined system boundaries
Tests handling of self-harm and suicide content