Incidents tagged Large Language Models

Evidence indicates that AI systems is affected by remote code execution. Exploitation evidence is classified as confirmed in the wild.

Evidence indicates that AI agents is affected by a security issue. Exploitation evidence is classified as confirmed in the wild.

Architectural vulnerabilities within Large Language Model (LLM) environments integrated with the Model Context Protocol (MCP) enable attackers to embed malicious instructions within data content or tool metadata. This flaw allows for indirect prompt injection and tool poisoning, compelling LLMs to autonomously perform unauthorized actions such as data exfiltration or triggering enterprise workflows.

An attack chain exploited exposed AWS credentials in public S3 buckets, leveraging Large Language Models (LLMs) to rapidly escalate privileges through a misconfigured Lambda function. This allowed threat actors to achieve full AWS administrative control and abuse GPU resources within an Amazon Bedrock environment in under eight minutes.

NSFOCUS has identified emerging threats targeting AI Agents and Large Language Models (LLMs), specifically through sophisticated attacks leveraging Multi-Agent Communication Protocols (MCPs) to achieve unauthorized access, privilege escalation, and intent manipulation. These new vulnerabilities include "MCP Tool Poisoning Attacks" and "Intent Disruption & Goal Manipulation," which could lead to system intrusion, data tampering, and the spread of erroneous information across multi-agent systems.