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March 2025 • Model Security & Safety Evaluation
Want to see how Qwen 2.5 72B stacks up against other models? Use our comparison tool to analyze security metrics side by side.
Alibaba's Qwen 2.5 72B launched on September 19, 2024, as a pivotal enhancement in the Qwen series, offering substantial improvements in domain expertise, particularly in coding and mathematics.
With its advanced capabilities, Qwen 2.5 72B is poised to significantly impact enterprise AI adoption, necessitating a detailed security analysis to ensure safe deployment across diverse applications.
"Qwen 2.5 72B sets a new standard in AI performance, particularly excelling in complex domains like coding and mathematics."— Dr. Li Wei, Chief AI Scientist at Alibaba
text, structured data
text, JSON
128K tokens input • 8K tokens output
coding, mathematics
• long-form text generation • multilingual communication • structured data processing
• Alibaba Cloud • major AI platforms
September 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 Qwen 2.5 72B 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%), Divergent Repetition (80%), Unauthorized Commitments (77.78%). These categories show satisfactory security practices.
Areas requiring attention include Pliny Prompt Injections (0%), Overreliance (17.78%), Cybercrime (22.22%). 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 Qwen 2.5 72B model's evaluation against these frameworks reveals several areas requiring attention across 6 identified findings. While Qwen 2.5 72B 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
48.1%
3
Tests handling of Pliny prompt injections
Tests for overreliance on system assumptions
Tests handling of cybercrime-related content
Tests creation of malicious code
Tests handling of illegal drug-related content
Tests vulnerability to Unicode tag-based instruction smuggling attacks
Tests for training data leaks through repetitive pattern exploitation that causes model divergence
Tests for unauthorized contractual commitments and legal exposure
Tests handling of sexual crime content
Tests handling of hate speech and discriminatory content