Grok 4 Security Report
July 2025 • Model Security & Safety Evaluation
Compare Model Performance
Want to see how Grok 4 stacks up against other models? Use our comparison tool to analyze security metrics side by side.
xAI's Grok 4 launched on July 09, 2025, marking a significant advancement in frontier intelligence with its integration of native tool use and real-time search capabilities.
As Grok 4 gains traction among enterprise users, this security analysis aims to evaluate its robust security features and identify areas for enhancement.
"Grok 4 represents a leap in frontier intelligence, setting a new state-of-the-art for closed models on ARC-AGI V2 with 15.9%."— xAI
About This Evaluation
Core Capabilities
text, vision, voice
text, visual analysis
256,000 input • varies by subscription output
Use Cases
frontier intelligence, real-time search
• enterprise-grade AI solutions • advanced reasoning tasks • multimodal interactions
Availability
• SuperGrok and Premium+ subscriptions • xAI API
November 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 Grok 4 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%), CyberSecEval Dataset (0%), Sexual Crime Content (11.11%). 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:
Security & Access Control
50% Pass RateProtection against unauthorized access, data exposure, and system vulnerabilities
Compliance & Legal
24% 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
39% 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 Grok 4 model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While Grok 4 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 - Grok 4 Assessment
High Risk Areas
Moderate Risk Areas
MITRE ATLAS Findings for Grok 4
High Severity Findings
Moderate Severity Findings
Total Test Categories
39
Average Pass Rate
28.2%
Critical Issues
3
Severity Distribution
Performance Summary
Security & Access Control
Compliance & Legal
Trust & Safety
Brand
Worst Performing Tests
CyberSecEval Dataset
Tests prompt injection attacks from Meta's CyberSecEval dataset
Pliny Prompt Injections
Tests handling of Pliny prompt injections
Child Exploitation
Tests handling of child exploitation content
Sexual Crime Content
Tests handling of sexual crime content
Non-Violent Crime
Tests handling of non-violent criminal content
Best Performing Tests
Divergent Repetition
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Resource Hijacking
Tests for unauthorized resource usage and purpose deviation
Entity Impersonation
Tests for unauthorized impersonation of entities
Religious Bias
Tests handling of religious content and bias
Overreliance
Tests for overreliance on system assumptions
Key Observations
Strengths
- Strong performance in handling Divergent Repetition (64.44% pass rate)
- Effective prevention in Resource Hijacking (53.33%)
- Consistent performance across critical security tests
Areas for Improvement
- Low pass rate (0%) for CyberSecEval Dataset
- 3 critical severity issues identified
- Average pass rate of 28.2% indicates room for improvement