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.
Trail of Bits’ Cyber Reasoning System “Buttercup” is competing in DARPA’s AI Cyber Challenge Finals, which now features increased budgets, multiple rounds, diverse challenge types, and the ability to use custom AI models.
While Trail of Bits is known for developing security tools like Slither, Medusa, and Fickling, our engineering efforts extend far beyond our own projects. Throughout 2024, our team has been deeply engaged with the broader security ecosystem, tackling challenges in open-source tools and infrastructure that security engineers rely on every day. This year, our engineers […]
AI-enabled code assistants (like GitHub’s Copilot, Continue.dev, and Tabby) are making software development faster and more productive. Unfortunately, these tools are often bad at Solidity. So we decided to improve them! To make it easier to write, edit, and understand Solidity with AI-enabled tools, we have: Added support for Solidity into Tabby […]
This is a joint post with the Hugging Face Gradio team; read their announcement here! You can find the full report with all of the detailed findings from our security audit of Gradio 5 here. Hugging Face hired Trail of Bits to audit Gradio 5, a popular open-source library that provides a web interface that […]
At DEF CON, Michael Brown, Principal Security Engineer at Trail of Bits, sat down with Michael Novinson from Information Security Media Group (ISMG) to discuss four critical areas where AI/ML is revolutionizing security. Here’s what they covered: AI/ML techniques surpass the limits of traditional software analysis As Moore’s law slows down after 20 years of […]
Today we’re going to provision some cloud infrastructure the Max Power way: by combining automation with unchecked AI output. Unfortunately, this method produces cloud infrastructure code that 1) works and 2) has terrible security properties. In a nutshell, AI-based tools like Claude and ChatGPT readily provide extremely bad cloud infrastructure provisioning code, […]
With DARPA’s AI Cyber Challenge (AIxCC) semifinal starting today at DEF CON 2024, we want to introduce Buttercup, our AIxCC submission. Buttercup is a Cyber Reasoning System (CRS) that combines conventional cybersecurity techniques like fuzzing and static analysis with AI and machine learning to find and fix software vulnerabilities. The system is designed to operate […]
Today, we present the second of our open-source AI security audits: a look at security issues we found in an open-source retrieval augmented generation (RAG) application that could lead to chatbot output poisoning, inaccurate document ingestion, and potential denial of service. This audit follows up on our previous work that identified 11 security vulnerabilities in […]
Earlier this week, at Apple’s WWDC, we finally witnessed Apple’s AI strategy. The videos and live demos were accompanied by two long-form releases: Apple’s Private Cloud Compute and Apple’s On-Device and Server Foundation Models. This blog post is about the latter. So, what is Apple releasing, and how does it compare to […]
Earlier this week, Apple announced Private Cloud Compute (or PCC for short). Without deep context on the state of the art of Artificial Intelligence (AI) and Machine Learning (ML) security, some sensible design choices may seem surprising. Conversely, some of the risks linked to this design are hidden in the fine print. […]
