Overview Synthetic data — artificially generated datasets used to train AI models — is becoming a popular way to avoid privacy issues and expand training material.But attackers are now targeting synthetic data generation pipelines to inject malicious patterns, bias, or… Read More ›
Overview In traditional cybersecurity, stealing source code is bad.In AI security, stealing model weights is catastrophic.The weights are the learned parameters that make your AI valuable — the result of millions in compute, proprietary data, and R&D.If an attacker exfiltrates… Read More ›
Overview To the human eye, an image might look normal. To an AI vision system, it could be the equivalent of a blinding flashbang. Adversarial images use carefully crafted, often imperceptible pixel changes to trick computer vision models into misclassifying… Read More ›
Overview Phishing is no longer just a poorly written email from a fake prince. Today, attackers are using large language models to generate highly persuasive, well-written, and personalized phishing messages — at scale. This new wave of AI-assisted phishing is… Read More ›
Overview Training data is the foundation of every AI system — but what happens when that data is subtly, strategically poisoned? Data poisoning is the act of injecting malicious, biased, or misleading data into a model’s training set, with the… Read More ›
Overview The evolution of AI has shifted from simple chat interfaces to autonomous agents — LLM-powered systems capable of planning, acting, and adapting without direct human input. While powerful for productivity, these agents also introduce a new class of security… Read More ›
Date: July 23, 2025Author: AI Defense LeagueCategory: Red Teaming | Penetration Testing | LLM Security Overview This post is the first in a new blog series focused on penetration testing and red teaming techniques for Large Language Models (LLMs). Today’s… Read More ›
Overview Open-source LLMs offer transparency and innovation — but they also create new risks when adversaries fine-tune these models for malicious purposes.This isn’t about prompt engineering or jailbreaking. It’s about retraining models to embed bias, backdoors, or harmful capabilities directly… Read More ›
Overview As LLMs become more capable and widely deployed, attackers are turning their attention to jailbreaking them — crafting prompts that bypass built-in safety restrictions. But what was once a fringe curiosity is now a full-fledged underground market: LLM jailbreaks… Read More ›
Overview Identity has always been at the core of trust and access — and now AI is shattering the line between real and synthetic. Today’s attackers use AI to generate realistic names, faces, documents, voices, and digital histories — giving… Read More ›
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