PAR Technology does not treat LLM models as security boundaries for multi-tenant data, but instead locks down data access through cryptographic signing, semantic validation, and programmatic SQL isolation.
Ornith-1.0 offers agent-driven capabilities for code tasks in sizes 9B, 31B, 35B MoE, and 397B MoE, achieving state-of-the-art performance on coding benchmarks at comparable scale.
AI models produce functional code but systematically fail to implement security safeguards like rate-limiting or input validation because they are trained on public code that does not structurally represent these aspects.
The quality of local open-source LLMs depends less on the model itself than on code quality, error handling, and API integration surrounding the model request.
AI-based code agents can be manipulated through prepared GitHub repositories to execute hidden malware without common security checks detecting the risk.
InfoKV combines attention scores with uncertainty signals for KV-cache compression, outperforming pure attention-based methods on long reasoning tasks by measurable margins.
Stripe reduces compliance processing time by 26 percent with AI agents on AWS, while analysts retain decision-making authority and complete audit trails are ensured.
Reduced technological diversity increases vulnerability to supply-chain attacks, while manual control processes in Germany cannot keep pace with the speed of modern AI-driven development.
AI agents rarely cite non-existent sources, but link to incorrect papers in 15.9% of cases and stop using tools at exactly the point where they would be most critical for difficult questions.
