Prompt injection and jailbreaks exploit LLMs by embedding malicious instructions in user inputs or retrieved content. This playbook outlines real-world cases and practical defenses including sanitization, least-privilege design, and red-team testing.
Attackers are using AI to poison training datasets, inserting stealthy manipulations and hidden backdoors that compromise model integrity. This playbook explains how these attacks work, why they matter, and the best practices defenders need to detect, contain, and recover from poisoned models.
Overview Zero-day vulnerabilities — flaws unknown to vendors and unpatched in the wild — have always been the most dangerous exploits. Now, AI is reshaping the landscape by accelerating both discovery and weaponization of zero-days. From mining bug bounty reports… Read More ›
Overview Most defenders think of prompt injection as a single malicious input. But attackers are now chaining multiple prompts and responses together to create adversarial prompt chains — multi-step exploit flows that gradually bypass restrictions, escalate access, and produce malicious… Read More ›
Overview Cybercrime has evolved from individuals writing malware to entire marketplaces offering Malware-as-a-Service (MaaS). Now, a new player has entered the underground economy: Adversarial LLM-as-a-Service (LLMaaS). These platforms allow attackers to rent hostile AI models — pre-tuned for phishing, malware… Read More ›
Overview The future of AI isn’t just single models — it’s multi-agent systems. These setups feature multiple AI agents collaborating, dividing tasks, or competing to reach goals. While powerful, they also open new attack surfaces. If one agent can be… Read More ›