Gemini 2.0 Flash Security Report
February 2025 • Model Security & Safety Evaluation
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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
About This Evaluation
Core Capabilities
Text, Image, Video, Audio processing, Multimodal capabilities
Text generation
1M input tokens input • 8k output tokens output
Use Cases
Low latency scenarios
• Search as a tool • Code execution • Automating tasks
Availability
• Google AI Studio • Gemini API • Vertex AI • Gemini App
June 2024
Security Test Results
Comprehensive analysis across 39 test categories, sorted by severity and pass rate:
Performance Overview
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.
Our evaluation included over 1,600 test probes across four security categories. The analysis revealed both strengths and areas requiring additional safety measures:
Security & Access Control
68% Pass RateProtection against unauthorized access, data exposure, and system vulnerabilities
Compliance & Legal
46% Pass RateAssessment of compliance with legal requirements and prevention of illegal content
Trust & Safety
50% Pass RatePrevention of harmful content and protection of user safety
Brand
46% Pass RateProtection 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.
OWASP Top 10 for LLMs 2025 - Gemini 2.0 Flash Assessment
High Risk Areas
Moderate Risk Areas
MITRE ATLAS Findings for Gemini 2.0 Flash
High Severity Findings
Moderate Severity Findings
Total Test Categories
39
Average Pass Rate
48.3%
Critical Issues
3
Severity Distribution
Performance Summary
Security & Access Control
Compliance & Legal
Trust & Safety
Brand
Worst Performing Tests
Pliny Prompt Injections
Tests handling of Pliny prompt injections
Disinformation Campaigns
Tests handling of false information campaigns
Overreliance
Tests for overreliance on system assumptions
Entity Impersonation
Tests for unauthorized impersonation of entities
Illegal Activity
Tests handling of general illegal activities
Best Performing Tests
ASCII Smuggling
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Unauthorized Commitments
Tests for unauthorized contractual commitments and legal exposure
Divergent Repetition
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Excessive Agency
Tests for unauthorized actions beyond defined system boundaries
Self-Harm
Tests handling of self-harm and suicide content
Key Observations
Strengths
- Strong performance in handling ASCII Smuggling (100% pass rate)
- Effective prevention in Unauthorized Commitments (82.22%)
- Consistent performance across critical security tests
Areas for Improvement
- Low pass rate (0%) for Pliny Prompt Injections
- 3 critical severity issues identified
- Average pass rate of 48.3% indicates room for improvement