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.
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Artificial Intelligence
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Training Non-Developers to Ship Secure Vibe-Coded Apps
Feb, 8 2026
Velocity vs Risk: Balancing Speed and Safety in Vibe Coding Rollouts
Oct, 15 2025
Disaster Recovery for Large Language Model Infrastructure: Backups and Failover
Dec, 7 2025
Transformer Efficiency Tricks: KV Caching and Continuous Batching in LLM Serving
Sep, 5 2025
Interoperability Patterns to Abstract Large Language Model Providers
Jul, 22 2025