Tag: LLM security

Training data poisoning lets attackers corrupt AI models with tiny amounts of fake data, leading to hidden backdoors and dangerous outputs. Learn how it works, real-world cases, and proven defenses to protect your LLMs.

Private prompt templates are a critical but overlooked security risk in AI systems. Learn how inference-time data leakage exposes API keys, user roles, and internal logic-and how to fix it with proven technical and governance measures.

Recent-posts

Training Non-Developers to Ship Secure Vibe-Coded Apps

Training Non-Developers to Ship Secure Vibe-Coded Apps

Feb, 8 2026

Velocity vs Risk: Balancing Speed and Safety in Vibe Coding Rollouts

Velocity vs Risk: Balancing Speed and Safety in Vibe Coding Rollouts

Oct, 15 2025

Disaster Recovery for Large Language Model Infrastructure: Backups and Failover

Disaster Recovery for Large Language Model Infrastructure: Backups and Failover

Dec, 7 2025

Transformer Efficiency Tricks: KV Caching and Continuous Batching in LLM Serving

Transformer Efficiency Tricks: KV Caching and Continuous Batching in LLM Serving

Sep, 5 2025

Interoperability Patterns to Abstract Large Language Model Providers

Interoperability Patterns to Abstract Large Language Model Providers

Jul, 22 2025