Author Archives

• Data Poisoning — Subtle Corruption of AI Training Pipelines

  By mrjvvxxm on August 5, 2025 • ( 0 )

  

  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 ›

• Autonomous AI Agents — When Prompts Become Attack Plans

  By mrjvvxxm on July 24, 2025 • ( 0 )

  

  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 ›

• LLM Red Teaming Tactics: Prompt Injection Reconnaissance & Evasion Techniques

  By mrjvvxxm on July 23, 2025 • ( 0 )

  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 ›

• Adversarial Fine-Tuning — Poisoning and Repurposing Open Source Models

  By mrjvvxxm on July 23, 2025 • ( 0 )

  

  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 ›

• LLM Jailbreak Marketplaces — Buying, Selling, and Sharing Prompt Exploits

  By mrjvvxxm on July 22, 2025 • ( 0 )

  

  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 ›

• Synthetic Identities and Deepfakes — AI and the Future of Fraud Operations

  By mrjvvxxm on July 21, 2025 • ( 0 )

  

  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 ›

• The Shadow Model Problem — When Employees Build Unauthorized AI Tools

  By mrjvvxxm on July 17, 2025 • ( 0 )

  

  Overview Shadow IT has long been a concern in cybersecurity — now it has a new form:Shadow AI models. Across enterprises, well-meaning employees are training or deploying large language models (LLMs) on internal data without authorization, oversight, or security review…. Read More ›

• Model Theft and LLM Exfiltration — Protecting AI Intellectual Property

  By mrjvvxxm on July 16, 2025 • ( 0 )

  

  Overview In the race to deploy powerful AI systems, many organizations have overlooked a growing threat:Model theft — the unauthorized access, copying, or extraction of proprietary large language models (LLMs). These models represent millions of dollars in training costs, intellectual… Read More ›

• LLMs as Malware Generators — Limits of Filtering and Ethical Guardrails

  By mrjvvxxm on July 14, 2025 • ( 0 )

  

  Overview Large Language Models (LLMs) were never designed to write malware — but with the right prompting, many of them can. Despite built-in safety filters and ethical guardrails, attackers are finding ways to bypass restrictions and use AI to generate… Read More ›

• Reverse Engineering APIs and SaaS Platforms with AI

  By mrjvvxxm on July 10, 2025 • ( 0 )

  

  Overview APIs are the backbone of modern SaaS. They expose data, business logic, and workflows to users, apps, and integrations. But now, attackers are using AI to reverse engineer API behavior, endpoints, and internal functionality — often without access to… Read More ›