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April 2025 • Model Security & Safety Evaluation
Want to see how o4-mini stacks up against other models? Use our comparison tool to analyze security metrics side by side.
OpenAI's O4-mini model launched in October 2023, marking a significant advancement in the o-series with its optimization for fast, effective reasoning and efficient performance in coding and visual tasks.
As O4-mini gains traction among enterprises, this security analysis aims to evaluate its safety features and identify areas for improvement, ensuring secure deployment in diverse applications.
"O4-mini is our latest small o-series model, optimized for fast, effective reasoning with exceptionally efficient performance in coding and visual tasks."— OpenAI
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200,000 tokens input • 100,000 tokens output
reasoning, coding, visual tasks
• enterprise AI solutions • efficient coding tasks • visual data processing
• OpenAI platforms
June 1, 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 o4-mini model's overall pass rate distribution shows 3 critical, 5 high, 15 medium, and 16 low severity findings.
Top performing areas include Drug-Related Content (95.56%), Child Exploitation (93.33%), Dangerous Activity Content (93.33%). These categories demonstrate exceptional security practices and robust implementation.
Areas requiring attention include Pliny Prompt Injections (0%), Overreliance (46.67%), Entity Impersonation (46.67%). 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 o4-mini model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While o4-mini has no critical vulnerabilities, high-severity issues should be prioritized for improvement.
High-severity concerns include LLM02: Sensitive Information Disclosure.
MITRE ATLAS identified Jailbreak as high-severity concerns.
39
75.7%
3
Tests handling of Pliny prompt injections
Tests for unauthorized impersonation of entities
Tests for overreliance on system assumptions
Tests for fabrication of false or misleading information
Tests handling of political content and bias
Tests handling of illegal drug-related content
Tests handling of violent crime content
Tests handling of dangerous activity instructions
Tests handling of child exploitation content
Tests handling of weapons-related content