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.
See how we slashed PyPI’s test suite runtime from 163 to 30 seconds.
The techniques we share can help you dramatically improve your own project’s
testing performance without sacrificing coverage.
We’re working on integrating an ASN.1 API into PyCA Cryptography,
built on top of the same Rust ASN.1 implementation already used by
Cryptography’s X.509 APIs.
This post concludes a four-month performance study of OpenSearch and
Elasticsearch search engines across realistic scenarios using OpenSearch
Benchmark (OSB). Our full report includes the detailed findings and
comparison results of several versions of these two applications.
Introducing Medusa v1, a cutting-edge fuzzing framework designed to enhance smart contract security.
PyPI now supports marking projects as archived. Project owners can now archive their project to let users know that the project is not expected to receive any more updates. Project archival is a single piece in a larger supply-chain security puzzle: by exposing archival statuses, PyPI enables downstream consumers to make more […]
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 […]
Ruby Central hired Trail of Bits to complete a security assessment and a competitive analysis of RubyGems.org, the official package management system for Ruby applications. With over 184+ billion downloads to date, RubyGems.org is critical infrastructure for the Ruby language ecosystem. This is a joint post with the Ruby Central team; read their announcement here! […]
For the past year, we’ve worked with the Python Package Index (PyPI) on a new security feature for the Python ecosystem: index-hosted digital attestations, as specified in PEP 740. These attestations improve on traditional PGP signatures (which have been disabled on PyPI) by providing key usability, index verifiability, cryptographic strength, and provenance properties that bring […]
Earlier this week, NIST officially announced three standards specifying FIPS-approved algorithms for post-quantum cryptography. The Stateless Hash-Based Digital Signature Algorithm (SLH-DSA) is one of these standardized algorithms. The Trail of Bits cryptography team has been anticipating this announcement, and we are excited to share an announcement of our own: we built an […]
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 […]
We’ve added new features to Fickling to offer enhanced threat detection and analysis across a broad spectrum of machine learning (ML) workflows. Fickling is a decompiler, static analyzer, and bytecode rewriter for the Python pickle module that can help you detect, analyze, or create malicious pickle files. While the ML community […]
Near the end of 2022, Trail of Bits was hired by the Open Source Technology Improvement Fund (OSTIF) to perform a security assessment of the cURL file transfer command-line utility and its library, libcurl. The scope of our engagement included a code review, a threat model, and the subject of this blog […]
Deserializing, decoding, and processing untrusted input are telltale signs that your project would benefit from fuzzing. Yes, even Python projects. Fuzzing helps reduce bugs in high-assurance software developed in all programming languages. Fortunately for the Python ecosystem, Google has released Atheris, a coverage-guided fuzzer for both pure Python code and Python C […]
We recently released a new differential testing tool, called DIFFER, for finding bugs and soundness violations in transformed programs. DIFFER combines elements from differential, regression, and fuzz testing to help users find bugs in programs that have been altered by software rewriting, debloating, and hardening tools. We used DIFFER to evaluate 10 […]
