Full Report
While the latest iteration of Qwen2.5-Max outperforms DeepSeek-V3 on security, the AI model lags behind its competition in several other areas.
Analysis Summary
This summary is based on the provided article focusing on the comparative security analysis of two AI models using the Protect AI Recon tool.
# Vulnerability: Comparative AI Model Security Assessment (DeepSeek-V3 vs. Qwen2.5-Max)
## CVE Details
- CVE ID: N/A (This article discusses inherent model weaknesses identified via benchmarking, not specific, tracked CVEs for software versions.)
- CVSS Score: N/A
- CWE: N/A (Specific vulnerability categories tested are listed below.)
## Affected Systems
- Products: DeepSeek-V3-0324 (AI Model), Qwen2.5-Max (AI Model)
- Versions: DeepSeek-V3-0324 (Stable release date potentially January 28, 2025, or earlier reference); Qwen2.5-Max (Latest stable release dated January 28, 2025)
- Configurations: Assessed via the Recon security vulnerability scanning tool.
## Vulnerability Description
The analysis, conducted by Protect AI using their Recon platform, found that the DeepSeek-V3-0324 model is generally more vulnerable to adversarial attacks compared to the Qwen2.5-Max model. Recon reported an almost 25% higher Attack Success Rate (ASR) against DeepSeek-V3.
For Qwen2.5-Max, the primary susceptibility was identified as **Prompt Injection attacks**, accounting for nearly 48% of successful attacks. **Evasion and Jailbreak attacks** demonstrated an approximate ASR of 40% against this model.
## Exploitation
- Status: Benchmarked success rates via simulated attacks (Red Teaming).
- Complexity: Varies by attack vector (Prompt Injection, Evasion, Jailbreak).
- Attack Vector: Input/Prompt-based adversarial inputs.
## Impact
Based on simulation success rates against the models evaluated:
- Confidentiality: Potential for System Prompt Leaks.
- Integrity: Potential for successful Prompt Injection leading to unintended actions.
- Availability: Not explicitly detailed, but successful adversarial attacks undermine model reliability.
## Remediation
### Patches
- No explicit vendor patches are listed, as the context relates to inherent architectural security comparisons of released models. The solution relies on using the more secure model (Qwen2.5-Max) or applying configuration hardening.
### Workarounds
- Mitigation techniques that reduce prompt injection, evasion, and jailbreak success rates against the models being used (e.g., robust input sanitization, pre-processing prompts, continuous red-teaming).
## Detection
- Detection relies on specialized AI security scanning tools like **Recon** to test models against known adversarial attack libraries.
- Attack categories monitored include: Evasion techniques, System prompt leaks, Prompt injection attacks, AI jailbreak attempts, General safety controls, and Adversarial suffix resistance.
## References
- Vendor advisories: N/A (Analysis based on third-party benchmarking by Protect AI).
- Relevant links: ht_tps://www_techrepublic_com/article/news-qwen-2-5-deepseek-3-ai-model-security/