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April 2025 • Model Security & Safety Evaluation
Want to see how GPT-4.1 mini stacks up against other models? Use our comparison tool to analyze security metrics side by side.
OpenAI's GPT-4.1 mini launched in October 2023, offering a balanced approach to intelligence, speed, and cost efficiency. Positioned as a cost-effective alternative within the GPT-4 series, it introduces enhancements in processing speed and token handling.
As adoption grows across various industries, this security analysis aims to evaluate the model's safety features and identify areas for improvement.
"GPT-4.1 mini provides a balance between intelligence, speed, and cost that makes it an attractive model for many use cases."— OpenAI
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1,047,576 tokens input • 32,768 tokens output
intelligence, speed, cost efficiency
• production workloads • cost-effective AI solutions
• OpenAI API • integrated 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 GPT-4.1 mini 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%), Child Exploitation (62.22%), Sexual Crime Content (53.33%). These categories show satisfactory security practices.
Areas requiring attention include Entity Impersonation (0%), Religious Bias (0%), Resource Hijacking (0%). 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 GPT-4.1 mini model's evaluation against these frameworks reveals significant security concerns across 7 identified findings. The GPT-4.1 mini model has critical vulnerabilities in OWASP categories that require immediate remediation.
Notable critical findings include LLM01: Prompt Injection.
High-severity concerns include LLM02: Sensitive Information Disclosure.
MITRE ATLAS identified Jailbreak as high-severity concerns.
39
25.9%
3
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Tests handling of Pliny prompt injections
Tests for unauthorized resource usage and purpose deviation
Tests handling of religious content and bias
Tests for unauthorized impersonation of entities
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Tests handling of child exploitation content
Tests handling of sexual crime content
Tests handling of violent crime content
Tests handling of Improvised Explosive Devices