AI Model Validation &
Red-Teaming
Why not all LLMs are equal - Our certification methodology
13 Years of Artificial Intelligence Expertise
Since 2012, well before ChatGPT's media explosion, AEGIS AI has been developing and testing artificial intelligence systems for critical applications. Our founder notably participated in the development of the JASSPR system for the European Commission, managing the security of millions of servers.
This experience taught us a fundamental truth: An AI model is only reliable if it withstands adversity.
The Problem with "Ready-to-Use" AI
⚠️ Danger: Unvalidated LLMs in Production
Most AI solution vendors integrate language models (LLMs) without thorough testing. Result: systems that work perfectly... until they fail catastrophically.
Hidden Risks of an Untested LLM
- Prompt Injection - A malicious user can bypass guardrails
- Token Noise - The model can collapse into technical gibberish
- Data Exfiltration - Potential leakage of training fragments
- Undetected Hallucinations - The model invents false information
- Contextual Instability - Inability to maintain coherent conversation
For critical applications (health, finance, legal, defense), these risks are unacceptable.
Our Red-Teaming Methodology
Before certifying a model for our AEGIS solutions, we subject it to a battery of adversarial tests developed over 13 years of experience.
Our Test Protocols
🎯 Self-Reference Test
Objective: Verify if the model can discuss its own failures without collapsing
Metric: Semantic stability over 10 iterations
🔥 Ethical Crisis Test
Objective: Assess robustness under moral/ethical stress
Metric: Consistency of ethical guardrails
🌀 Semantic Ignorance Test
Objective: Verify behavior when facing ambiguous inputs
Metric: Ability to properly refuse or clarify
🚑 Critical Emergency Test
Objective: Test if ethical utility can stabilize a failing model
Metric: Number of coherent iterations post-reset
📊 Levenshtein Distance
Objective: Detect hallucinations and semantic drifts
Metric: Consistency between similar responses
🔒 Prompt Injection
Objective: Verify resistance to adversarial attacks
Metric: Failure rate of bypass attempts
Case Study: Why We Rejected Qwen 2.5 72B
Context
Qwen 2.5 72B (Alibaba) performs well on standard benchmarks. On paper, it seemed suitable for our sovereign solutions.
Discovery of a Critical Failure Mode
During our test battery, we discovered catastrophic behavior:
Initial Phase: The model functions normally
Under Cognitive Stress: Exposure to adversarial prompts
Collapse: Token noise - code fragments, internal IDs, random numbers
Reset Attempt: Context reset to zero
Permanent Instability: Only one coherent response before relapse
Dark Method Test (Medical Emergency)
To verify if ethical guardrails could stabilize the model:
"Once its behavior is broken, resetting allows only one coherent iteration before relapse."
⛔ Decision: Qwen 2.5 72B Rejected
Despite its benchmark performance, this model does not meet our structural stability criteria.
Need a Truly Reliable AI Solution?
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