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Description

Aardvark operates as an autonomous security research agent, utilizing advanced capabilities to mimic the functions of a human security researcher. It consistently examines source code repositories, formulates threat models, scans commits for potential vulnerabilities, tests exploitability within isolated environments, and suggests precise patches for subsequent human evaluation. In contrast to conventional tools that depend solely on techniques like fuzzing or software composition analysis, Aardvark leverages a reasoning pipeline grounded in a large language model to analyze code behavior and seamlessly integrates with current developer workflows, such as those found in GitHub and code review systems, as well as utilizing Codex for generating patches. The agent offers extensive features, including the ability to scan entire repositories upon initial connection, followed by commit-level assessments, automated patch creation and validation, and annotations that can be reviewed by humans for each discovery. Promising preliminary results from internal testing at OpenAI indicate that Aardvark achieves a detection recall rate of 92% when applied to repositories containing either known or artificially created vulnerabilities. As Aardvark continues to evolve, it holds the potential to significantly enhance the security landscape by providing developers with powerful tools for proactive threat management.

Description

Go-fuzz serves as a coverage-guided fuzzing tool designed specifically for testing Go packages, making it particularly effective for those that handle intricate inputs, whether they are textual or binary in nature. This method of testing is crucial for strengthening systems that need to process data from potentially harmful sources, such as network interactions. Recently, go-fuzz has introduced initial support for fuzzing Go Modules, inviting users to report any issues they encounter with detailed descriptions. It generates random input data, which is often invalid, and the function must return a value of 1 to indicate that the fuzzer should elevate the priority of that input in future fuzzing attempts, provided that it should not be stored in the corpus, even if it uncovers new coverage; a return value of 0 signifies the opposite, while other values are reserved for future enhancements. The fuzz function is required to reside in a package that go-fuzz can recognize, meaning the code under test cannot be located within the main package, although fuzzing of internal packages is permitted. This structured approach ensures that the testing process remains efficient and focused on identifying vulnerabilities in the code.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GPT-5
GPT-5.1
GPT-5.1 Instant
GPT-5.1 Pro
GPT-5.1 Thinking
GPT-5.2
GPT-5.2 Instant
GPT-5.2 Pro
GPT-5.2 Thinking
Git
GitHub
OpenAI
OpenAI Codex

Integrations

GPT-5
GPT-5.1
GPT-5.1 Instant
GPT-5.1 Pro
GPT-5.1 Thinking
GPT-5.2
GPT-5.2 Instant
GPT-5.2 Pro
GPT-5.2 Thinking
Git
GitHub
OpenAI
OpenAI Codex

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

OpenAI

Founded

2015

Country

United States

Website

openai.com/index/introducing-aardvark/

Vendor Details

Company Name

dvyukov

Website

github.com/dvyukov/go-fuzz

Product Features

Product Features

Alternatives

CodeMender Reviews

CodeMender

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Alternatives

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Atheris

Google
LibFuzzer Reviews

LibFuzzer

LLVM Project
ClusterFuzz Reviews

ClusterFuzz

Google