ai-security

Agentic Identity and Privilege Abuse

AI agents now inherit more privilege than they need — and incidents like the Microsoft 38TB SAS-token leak and ServiceNow's Now Assist flaw show what happens when that privilege gets abused.

Agent Tool Misuse and Exploitation

Attackers don't need to hack AI agents — they just redirect their own tools. Here's how tool misuse works, real 2025 incidents, and how to stop it.

Agent Goal Hijacking: Redirecting Autonomous AI Objectives

Attackers are hijacking autonomous AI agents by planting instructions in content they read—no exploit needed. Here's how it works, real 2025 incidents, and defenses.

LLM Misinformation: Security Risks of Hallucinated Outputs

LLM hallucinations aren't just AI trivia — they invent packages attackers squat on, fake CVEs, and false advisories that have already cost real companies real money.

LLM Unbounded Consumption: Resource Exhaustion Attacks

How attackers exploit token-based pricing and growing context windows to exhaust LLM compute and inflate cloud bills — and the concrete limits that stop them.

Jul 19, 20269 min read

LLM Vector and Embedding Weaknesses

Embeddings aren't anonymized math — Vec2Text recovers 92% of text from vectors, and OWASP's LLM08:2025 now names inversion, poisoning, and exposed vector DBs as core AI risks.

LLM System Prompt Leakage

System prompts often hide business logic and secrets. Here's how attackers extract them, real 2023-2024 incidents, and how to stop leaks before they reach production.

Model Theft: Protecting Proprietary LLMs from Extraction ...

A $20 API attack can clone a production LLM's embeddings. Here's how model extraction works, real incidents from LLaMA to DeepSeek, and how to protect proprietary models.

LLM Supply Chain Vulnerabilities

Malicious model files, poisoned datasets, and compromised ML packages are the new software supply chain frontier. Here is how these LLM attacks actually work.

Training Data Poisoning Attacks on Machine Learning Models

A $60 domain purchase or 0.001% of training tokens can silently corrupt an ML model. Here's how training data poisoning attacks work and how to defend against them.

Jul 18, 20267 min read

Sensitive Information Disclosure in LLM Applications

From Samsung's ChatGPT leak to RAG pipelines with no access controls, sensitive information disclosure is now a top LLM security risk. Here's how it happens and how to stop it.

Jul 18, 20268 min read