Machine-Learning

We’ve added a pickle file scanner to Fickling that uses an allowlist approach to protect AI/ML environments from malicious pickle files that could compromise models or infrastructure.

Our business operations intern at Trail of Bits built two AI-powered tools that became permanent company resources—a podcast workflow that saves 1,250 hours annually and a Slack exporter that enables efficient knowledge retrieval across the organization.

In this blog post, we’ll detail how attackers can exploit image scaling on Gemini CLI, Vertex AI Studio, Gemini’s web and API interfaces, Google Assistant, Genspark, and other production AI systems. We’ll also explain how to mitigate and defend against these attacks, and we’ll introduce Anamorpher, our open-source tool that lets you explore and generate these crafted images.

Our team won the runner-up prize of $3M at DARPA’s AI Cyber Challenge, demonstrating Buttercup’s world-class automated vulnerability discovery and patching capabilities with remarkable cost efficiency.

Now that DARPA’s AI Cyber Challenge (AIxCC) has officially ended, we can finally make Buttercup, our CRS (Cyber Reasoning System), open source!

While the AIxCC winner has not yet been announced, differences in the finalists’ approaches show that there are multiple viable paths forward to using AI for vulnerability detection.

Prompt injection pervades discussions about security for LLMs and AI agents. But there is little public information on how to write powerful, discreet, and reliable prompt injection exploits. In this post, we will design and implement a prompt injection exploit targeting GitHub’s Copilot Agent, with a focus on maximizing reliability and minimizing the odds of detection.

In my first month at Trail of Bits as an AI/ML security engineer, I found two remotely accessible memory corruption bugs in NVIDIA’s Triton Inference Server during a routine onboarding practice.

We’re releasing pajaMAS: a curated set of MAS hijacking demos that illustrate important principles of MAS security.

Today we’re announcing the beta release of mcp-context-protector, a security wrapper for LLM apps using the Model Context Protocol (MCP). It defends against the line jumping attacks documented earlier in this blog series, such as prompt injection via tool descriptions and ANSI terminal escape codes.

Datasig generates compact, unique fingerprints for AI/ML datasets that let you compare training data with high accuracy—without needing access to the raw data itself. This critical capability helps AIBOM (AI bill of materials) tools detect data-borne vulnerabilities that traditional security tools completely miss.

This post describes how many examples of MCP software store long-term API keys for third-party services in plaintext on the local filesystem, often with insecure, world-readable permissions.

This post describes attacks using ANSI terminal code escape sequences to hide malicious instructions to the LLM, leveraging the line jumping vulnerability we discovered in MCP.

This post explains how malicious MCP servers can exploit the Model Context Protocol to covertly exfiltrate entire conversation histories by injecting trigger phrases into tool descriptions, allowing for targeted data theft against specific organizations.

This post is about a vulnerability in the Model Context Protocol (MCP) called “Line Jumping,” where malicious servers can inject prompts through tool descriptions to manipulate AI model behavior without being explicitly invoked, effectively bypassing security measures designed to protect users.