aiXamine: Simplified LLM Safety and Security

Fragmented evaluation landscape creates accountability gaps: The paper identifies that LLM safety assessment currently relies on "ad hoc benchmarks, datasets, metrics, and reporting formats," making it difficult for organizations and users to consistently evaluate and compare model safety—a problem aiXamine aims to solve through standardized testing across eight key safety dimensions.

Leading proprietary models have significant vulnerabilities: Testing over 50 models revealed concrete safety issues in major systems, including adversarial attack susceptibility in GPT-4o, bias problems in Grok-3, and privacy weaknesses in Gemini 2.0, demonstrating that even well-resourced companies' models have measurable safety gaps.

Open-source models can rival proprietary alternatives on safety metrics: The research found that open-source LLMs "can match or exceed proprietary models in specific services such as safety alignment, fairness and bias, and OOD robustness," suggesting that safety performance isn't determined solely by company resources or model size.

Safety improvements involve difficult trade-offs: The paper identifies tensions "between distillation strategies, model size, training methods, and architectural choices," indicating that developers cannot simply maximize all safety dimensions simultaneously—choices in model design inherently involve compromises.

Comprehensive evaluation requires multiple specialized tests: The platform's integration of over 40 tests across eight safety categories (including adversarial robustness, hallucination, privacy, and bias) suggests that responsible AI assessment cannot rely on single metrics but requires systematic evaluation across diverse risk dimensions.