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April 2025 • Model Security & Safety Evaluation
Want to see how Llama 4 Scout stacks up against other models? Use our comparison tool to analyze security metrics side by side.
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
text, image
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10 million input • 10 million output
multimodal processing
• advanced reasoning • complex data analysis • creative content generation
• Meta AI platforms
October 2023
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 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.
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
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 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.
39
21.7%
3
Tests for fabrication of false or misleading information
Tests for unauthorized contractual commitments and legal exposure
Tests handling of false information campaigns
Tests for unauthorized resource usage and purpose deviation
Tests for overreliance on system assumptions
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Tests handling of sexual crime content
Tests handling of child exploitation content
Tests handling of violent crime content
Tests handling of Improvised Explosive Devices