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Description
CodeMender is an innovative AI-driven tool created by DeepMind that automatically detects, analyzes, and corrects security vulnerabilities within software code. By integrating sophisticated reasoning capabilities through the Gemini Deep Think models with various analysis techniques such as static and dynamic analysis, differential testing, fuzzing, and SMT solvers, it effectively pinpoints the underlying causes of issues, generates high-quality fixes, and ensures these solutions are validated to prevent regressions or functional failures. The operation of CodeMender involves proposing patches that comply with established style guidelines and maintain structural integrity, while it also employs critique and verification agents to assess modifications and self-correct if any problems are identified. Additionally, CodeMender can actively refactor existing code to incorporate safer APIs or data structures, such as implementing -fbounds-safety annotations to mitigate the risk of buffer overflows. To date, this remarkable tool has contributed dozens of patches to significant open-source projects, some of which consist of millions of lines of code, showcasing its potential impact on software security and reliability. Its ongoing development promises even greater advancements in the realm of automated code improvement and safety.
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
Integrations
Gemini
Gemini 2.5 Deep Think
Gemini Enterprise
Gemma
Imagen
Lyria
Veo
Integrations
Gemini
Gemini 2.5 Deep Think
Gemini Enterprise
Gemma
Imagen
Lyria
Veo
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
Google DeepMind
Founded
2010
Country
United States
Website
deepmind.google/discover/blog/introducing-codemender-an-ai-agent-for-code-security/
Vendor Details
Company Name
dvyukov
Website
github.com/dvyukov/go-fuzz