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Average Ratings 0 Ratings
Description
Devirt offers a specialized service for binary devirtualization and reverse engineering tailored for security researchers and malware analysts. Programs protected by tools like VMProtect and Themida obscure their internal logic by using private virtual machines. The Devirt service monitors the execution of these protected applications, meticulously reconstructing the state and control flow of the virtual machine, transforming that behavior into native functions, and validating them against the original code.
Clients have the flexibility to select their desired output format, which may include an IDA database, annotated pseudocode, a comprehensive reverse engineering report, LLVM IR, or structured JSON. Each recovery is accompanied by a coverage ledger that distinguishes between code that has been validated under specified assumptions and code that remains unresolved, dependent on runtime conditions, or permanently limited, enabling analysts to pinpoint exactly where the evidence concludes. This structured approach enhances the analysis process, providing clarity and precision in understanding the devirtualized code.
Description
LibFuzzer serves as an in-process, coverage-guided engine for evolutionary fuzzing. By being linked directly with the library under examination, it injects fuzzed inputs through a designated entry point, or target function, allowing it to monitor the code paths that are executed while creating variations of the input data to enhance code coverage. The coverage data is obtained through LLVM’s SanitizerCoverage instrumentation, ensuring that users have detailed insights into the testing process. Notably, LibFuzzer continues to receive support, with critical bugs addressed as they arise. To begin utilizing LibFuzzer with a library, one must first create a fuzz target—this function receives a byte array and interacts with the API being tested in a meaningful way. Importantly, this fuzz target operates independently of LibFuzzer, which facilitates its use alongside other fuzzing tools such as AFL or Radamsa, thereby providing versatility in testing strategies. Furthermore, the ability to leverage multiple fuzzing engines can lead to more robust testing outcomes and clearer insights into the library's vulnerabilities.
API Access
Has API
No
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Atheris
No
C
No
C++
No
ClusterFuzz
No
Fuzzbuzz
No
Google ClusterFuzz
No
Jazzer
No
Integrations
Atheris
Yes
C
Yes
C++
Yes
ClusterFuzz
Yes
Fuzzbuzz
Yes
Google ClusterFuzz
Yes
Jazzer
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Devirt
Founded
2026
Website
devirt.re
Vendor Details
Company Name
LLVM Project
Founded
2003
Website
llvm.org/docs/LibFuzzer.html
Product Features
Cybersecurity
AI / Machine Learning
No
Behavioral Analytics
No
Endpoint Management
No
IOC Verification
No
Incident Management
No
Tokenization
No
Vulnerability Scanning
No
Whitelisting / Blacklisting
No