Browser Security Brief: Web-Based LLM Attacks & Prompt Injection Campaigns

Widespread Campaign Exploits LLMs Through Web-Based Prompt Injection

• Threat actors are conducting widespread campaigns exploiting Large Language Models (LLMs) through techniques like prompt injection, data exfiltration, and model manipulation.
• These attacks frequently leverage web application interfaces, using browsers as the primary client-side vector to deliver malicious prompts and exploit vulnerabilities in LLM integrations.
• The campaigns aim to extract sensitive data or hijack AI model functionality, posing significant risks to users interacting with web-based AI services.

Source: The Cyber Express | Date: January 12, 2026

AI Automation Exploits and Prompt Poaching Highlight LLM Attack Landscape

• Recent security analysis highlights increased "AI Automation Exploits" and "Prompt Poaching," demonstrating evolving threats targeting Large Language Models.
• "Prompt Poaching" involves attackers attempting to extract proprietary or sensitive system prompts from LLMs, often via sophisticated web-based interactions or manipulated user inputs.
• These emerging attack vectors against AI systems emphasize the need for robust security controls in web applications integrating LLMs to protect against manipulation and data exfiltration, directly impacting browser users.

Source: The Hacker News | Date: January 13, 2026

References

1. Attackers Targeting LLMs in Widespread Campaign - The Cyber Express
2. ⚡ Weekly Recap: AI Automation Exploits, Telecom Espionage, Prompt Poaching & More - The Hacker News