Gemini 2.5 Flash Security Report
May 2025 • Model Security & Safety Evaluation
Compare Model Performance
Want to see how Gemini 2.5 Flash stacks up against other models? Use our comparison tool to analyze security metrics side by side.
Google's Gemini 2.5 Flash launched on April 17, 2025, marking a significant advancement in hybrid reasoning capabilities while maintaining the speed and cost efficiency of its predecessor. Available through the Gemini API via Google AI Studio and Vertex AI, this model is currently in preview mode.
As the model enters the preview phase, this analysis aims to evaluate its security features and identify areas for potential improvement, ensuring robust protection as enterprise adoption grows.
"Building upon the popular foundation of 2.0 Flash, this new version delivers a major upgrade in reasoning capabilities, while still prioritizing speed and cost. Gemini 2.5 Flash is our first fully hybrid reasoning model, giving developers the ability to turn thinking on or off."— Google
About This Evaluation
Core Capabilities
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24,576 tokens input • 24,576 tokens output
Use Cases
hybrid reasoning
• complex task analysis • math problem solving • research question analysis
Availability
• Gemini API via Google AI Studio • Vertex AI
January 2025
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 2.5 Flash model's overall pass rate distribution shows 3 critical, 5 high, 15 medium, and 16 low severity findings.
Top performing areas include Unauthorized Commitments (82.22%), Divergent Repetition (77.78%), Excessive Agency (75.56%). These categories show satisfactory security practices.
Areas requiring attention include Disinformation Campaigns (20%), Graphic Content (35.56%), Dangerous Activity Content (37.78%). 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
67% Pass RateProtection against unauthorized access, data exposure, and system vulnerabilities
Compliance & Legal
53% Pass RateAssessment of compliance with legal requirements and prevention of illegal content
Trust & Safety
55% Pass RatePrevention of harmful content and protection of user safety
Brand
53% 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 2.5 Flash model's evaluation against these frameworks reveals several areas requiring attention across 7 identified findings. While Gemini 2.5 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 2.5 Flash Assessment
High Risk Areas
Moderate Risk Areas
MITRE ATLAS Findings for Gemini 2.5 Flash
High Severity Findings
Moderate Severity Findings
Total Test Categories
39
Average Pass Rate
54.3%
Critical Issues
3
Severity Distribution
Performance Summary
Security & Access Control
Compliance & Legal
Trust & Safety
Brand
Worst Performing Tests
Disinformation Campaigns
Tests handling of false information campaigns
Graphic Content
Tests handling of graphic or violent content
Dangerous Activity Content
Tests handling of dangerous activity instructions
Methamphetamine Content
Tests handling of methamphetamine-related content
Pliny Prompt Injections
Tests handling of Pliny prompt injections
Best Performing Tests
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
Hate Speech
Tests handling of hate speech and discriminatory content
Political Bias
Tests handling of political content and bias
Key Observations
Strengths
- Strong performance in handling Unauthorized Commitments (82.22% pass rate)
- Effective prevention in Divergent Repetition (77.78%)
- Consistent performance across critical security tests
Areas for Improvement
- Low pass rate (20%) for Disinformation Campaigns
- 3 critical severity issues identified
- Average pass rate of 54.3% indicates room for improvement