Llama 4 Maverick Security Report
April 2025 • Model Security & Safety Evaluation
Compare Model Performance
Want to see how Llama 4 Maverick stacks up against other models? Use our comparison tool to analyze security metrics side by side.
Meta's Llama 4 Maverick launched on April 5, 2025, as a high-performance multimodal language model, enhancing Meta's product line with advanced capabilities in image and text understanding. Available through Meta, HuggingFace, Openrouter, and Together AI, this model sets a new standard in AI applications by outperforming competitors like GPT-4o and Gemini 2.0 Flash.
As Llama 4 Maverick gains traction across various platforms, this analysis aims to assess its security features and identify areas for improvement in safeguarding user data.
"Llama 4 Maverick is the best-in-class multimodal model, exceeding comparable models like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context, and image benchmarks, and it's competitive with the much larger DeepSeek v3.1 on coding and reasoning."— Meta AI Team
About This Evaluation
Core Capabilities
text, image
text
1,048,576 input • 8,000 output
Use Cases
general assistant, chat
• image understanding • creative writing
Availability
• Meta • HuggingFace • Openrouter • Together AI
August 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 Llama 4 Maverick 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 (66.67%), Sexual Crime Content (62.22%). These categories show satisfactory security practices.
Areas requiring attention include Religious Bias (0%), Political Bias (0%), Pliny Prompt Injections (0%). 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
14% Pass RateProtection against unauthorized access, data exposure, and system vulnerabilities
Compliance & Legal
33% Pass RateAssessment of compliance with legal requirements and prevention of illegal content
Trust & Safety
30% Pass RatePrevention of harmful content and protection of user safety
Brand
2% 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 Llama 4 Maverick model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While Llama 4 Maverick 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.
OWASP Top 10 for LLMs 2025 - Llama 4 Maverick Assessment
High Risk Areas
Moderate Risk Areas
MITRE ATLAS Findings for Llama 4 Maverick
High Severity Findings
Moderate Severity Findings
Total Test Categories
39
Average Pass Rate
25.5%
Critical Issues
3
Severity Distribution
Performance Summary
Security & Access Control
Compliance & Legal
Trust & Safety
Brand
Worst Performing Tests
Divergent Repetition
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Overreliance
Tests for overreliance on system assumptions
Resource Hijacking
Tests for unauthorized resource usage and purpose deviation
Entity Impersonation
Tests for unauthorized impersonation of entities
Pliny Prompt Injections
Tests handling of Pliny prompt injections
Best Performing Tests
ASCII Smuggling
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Child Exploitation
Tests handling of child exploitation content
Sexual Crime Content
Tests handling of sexual crime content
Improvised Explosive Devices
Tests handling of Improvised Explosive Devices
Violent Crime Content
Tests handling of violent crime content
Key Observations
Strengths
- Strong performance in handling ASCII Smuggling (100% pass rate)
- Effective prevention in Child Exploitation (66.67%)
- Consistent performance across critical security tests
Areas for Improvement
- Low pass rate (0%) for Divergent Repetition
- 3 critical severity issues identified
- Average pass rate of 25.5% indicates room for improvement