CodeMenderGoogle DeepMind
|
LibFuzzerLLVM Project
|
|||||
Related Products
|
||||||
About
CodeMender is an AI-powered agent developed by DeepMind for automatically finding, diagnosing, and patching security vulnerabilities in software code. It combines advanced reasoning abilities (via Gemini Deep Think models) with program analysis tools, static analysis, dynamic analysis, differential testing, fuzzing, and SMT solvers, to identify root causes of flaws, generate high-quality fixes, and validate them to avoid regressions or functional breakage. CodeMender operates by proposing patches that adhere to style rules and structural correctness, and then uses critique and verification agents to check changes and self-correct if issues arise. It can also proactively rewrite existing code using safer APIs or data structures (for example, applying -fbounds-safety annotations to prevent buffer overflows). To date, CodeMender has upstreamed dozens of patches in large open source projects (including ones with millions of lines of code).
|
About
LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine. LibFuzzer is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entry point (or target function); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage. The code coverage information for libFuzzer is provided by LLVM’s SanitizerCoverage instrumentation. LibFuzzer is still fully supported in that important bugs will get fixed. The first step in using libFuzzer on a library is to implement a fuzz target, a function that accepts an array of bytes and does something interesting with these bytes using the API under test. Note that this fuzz target does not depend on libFuzzer in any way so it is possible and even desirable to use it with other fuzzing engines like AFL and/or Radamsa.
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
Software engineers, security teams, and maintainers looking for a solution to automate detection and secure patching of vulnerabilities in their codebases with AI assistance
|
Audience
Users requiring a fuzzing engine to analyze their code and applications
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
Free
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationGoogle DeepMind
Founded: 2010
United States
deepmind.google/discover/blog/introducing-codemender-an-ai-agent-for-code-security/
|
Company InformationLLVM Project
Founded: 2003
llvm.org/docs/LibFuzzer.html
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Gemini
Gemini 2.5 Deep Think
Gemini Enterprise
Gemma
Google ClusterFuzz
|
Integrations
Atheris
C
C++
ClusterFuzz
Fuzzbuzz
Gemini
Gemini 2.5 Deep Think
Gemini Enterprise
Gemma
Google ClusterFuzz
|
|||||
|
|
|