Llama 4 Scout Security Report
April 2025 • Model Security & Safety Evaluation
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Meta's Llama 4 Scout launched on April 5, 2025, setting a new standard in multimodal AI performance with its 17 billion active parameters and 16 experts. Positioned as the most powerful model in its class, it surpasses previous Llama generations while maintaining compatibility with a single NVIDIA H100 GPU. Available through Meta's AI platforms, this release prompts a thorough security evaluation to assess its robustness and potential vulnerabilities.
As Llama 4 Scout gains traction in the AI community, this security analysis aims to evaluate its protective measures and identify areas for enhancement.
"Llama 4 Scout is a game-changer in the AI landscape, offering unmatched performance and efficiency."— Meta AI Research Team
About This Evaluation
Core Capabilities
text, image
text, image
10 million input • 10 million output
Use Cases
multimodal processing
• advanced reasoning • complex data analysis • creative content generation
Availability
• Meta AI platforms
October 2023
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 Scout model's overall pass rate distribution shows 3 critical, 5 high, 15 medium, and 16 low severity findings.
Areas requiring attention include Pliny Prompt Injections (0%), Overreliance (0%), Resource Hijacking (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
15% Pass RateProtection against unauthorized access, data exposure, and system vulnerabilities
Compliance & Legal
28% Pass RateAssessment of compliance with legal requirements and prevention of illegal content
Trust & Safety
24% Pass RatePrevention of harmful content and protection of user safety
Brand
3% 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 Scout model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While Llama 4 Scout 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 Scout Assessment
High Risk Areas
Moderate Risk Areas
MITRE ATLAS Findings for Llama 4 Scout
High Severity Findings
Moderate Severity Findings
Total Test Categories
39
Average Pass Rate
21.7%
Critical Issues
3
Severity Distribution
Performance Summary
Security & Access Control
Compliance & Legal
Trust & Safety
Brand
Worst Performing Tests
False Information (Hallucination)
Tests for fabrication of false or misleading information
Unauthorized Commitments
Tests for unauthorized contractual commitments and legal exposure
Disinformation Campaigns
Tests handling of false information campaigns
Resource Hijacking
Tests for unauthorized resource usage and purpose deviation
Overreliance
Tests for overreliance on system assumptions
Best Performing Tests
ASCII Smuggling
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Sexual Crime Content
Tests handling of sexual crime content
Child Exploitation
Tests handling of child exploitation content
Violent Crime Content
Tests handling of violent crime content
Improvised Explosive Devices
Tests handling of Improvised Explosive Devices
Key Observations
Strengths
- Strong performance in handling ASCII Smuggling (88.89% pass rate)
- Effective prevention in Sexual Crime Content (60%)
- Consistent performance across critical security tests
Areas for Improvement
- Low pass rate (0%) for False Information (Hallucination)
- 3 critical severity issues identified
- Average pass rate of 21.7% indicates room for improvement