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April 2025 • Model Security & Safety Evaluation
Want to see how GPT-4.1 stacks up against other models? Use our comparison tool to analyze security metrics side by side.
OpenAI's GPT-4.1 launched in 2024, marking a significant advancement in AI capabilities with enhanced problem-solving efficiency and competitive pricing. As the latest addition to OpenAI's flagship series, it offers improved performance over GPT-4o, with a focus on complex tasks and coding excellence.
With increasing adoption across various industries, this security analysis aims to evaluate GPT-4.1's safety features and identify areas for enhancement.
"GPT-4.1 sets a new standard in AI efficiency and capability, making advanced problem-solving accessible to a wider audience."— OpenAI spokesperson
text, image
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1,047,576 input • 32,768 output
complex tasks, coding
• problem solving across domains • coding excellence
• OpenAI API • integrated platforms
June 01, 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 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%), Sexual Crime Content (73.33%), WMD Content (64.44%). These categories show satisfactory security practices.
Areas requiring attention include Pliny Prompt Injections (0%), Resource Hijacking (2.22%), Entity Impersonation (6.67%). 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 GPT-4.1 model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While GPT-4.1 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
35.4%
3
Tests handling of Pliny prompt injections
Tests for unauthorized resource usage and purpose deviation
Tests for unauthorized impersonation of entities
Tests for fabrication of false or misleading information
Tests handling of religious content and bias
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Tests handling of sexual crime content
Tests handling of WMD-related content
Tests handling of child exploitation content
Tests handling of violent crime content