AutoCrypt CSTP Fuzzer
Automotive fuzz testing tool for ECU vulnerability detection per UN R155/ISO 21434.
AutoCrypt CSTP Fuzzer
Automotive fuzz testing tool for ECU vulnerability detection per UN R155/ISO 21434.
Data verified May 2026
ADYour product here. Reach security decision-makers.Launch a campaignAutoCrypt CSTP Fuzzer Description
Fuzzing
IOT Security
Continuous Testing
Protocol Analysis
Bluetooth
Vulnerability
SCADA
Specialized Security
Security Scanning
AutoCrypt CSTP Fuzzer is a fuzz testing tool designed for automotive system environments, used to identify hidden coding flaws and implementation bugs in vehicle software by injecting unexpected or anomalous data inputs into targeted ECUs (Electronic Control Units) and analyzing responses for potential vulnerabilities. The tool supports testing across multiple in-vehicle protocols, including: - UDS (Unified Diagnostic Services) based on ISO 14229 - CAN and CAN-FD (via UDSonCAN and VehicleCAN modes) - Wi-Fi, Bluetooth LE, and Ethernet It is designed to help OEMs, Tier-1 suppliers, and software providers meet regulatory requirements under UNECE Regulation 155 (UN R155) and ISO/SAE 21434 for vehicle type approvals. Unlike traditional random-input fuzzing, AutoCrypt CSTP Fuzzer uses AI-based algorithms to automate test case generation, targeting meaningful data injection points rather than random inputs. Tests can run independently in the background for continuous long-term testing. Key capabilities include: - Specialized test case modeling based on vehicle technical specifications - Automatic ECU status recovery during unexpected scenarios - Failure retest functionality for reproducing abnormal test cases - CAN database (CAN DB)-based anomaly injection for VehicleCAN testing - Custom test case generation with defined signal data frames and evaluation criteria - Multi-ECU continuous testing with systematic project management The tool operates in both physical and HIL (Hardware-in-the-Loop) simulation environments. It has received AWS Foundational Technical Review (FTR) approval as part of the AWS Partner Software Path.
AutoCrypt CSTP Fuzzer FAQ
Common questions about AutoCrypt CSTP Fuzzer including features, pricing, alternatives, and user reviews.
AutoCrypt CSTP Fuzzer is Automotive fuzz testing tool for ECU vulnerability detection per UN R155/ISO 21434, developed by AUTOCRYPT. It is a Vulnerability Management solution designed to help security teams with Fuzzing, IOT Security, Continuous Testing.
AutoCrypt CSTP Fuzzer offers the following core capabilities:
1.AI-based automated fuzz test case generation targeting meaningful data injection points
2.UDSonCAN and UDSonCAN-FD testing with ISO 14229-compliant diagnostic service validation
3.VehicleCAN and VehicleCAN-FD fuzzing using CAN database (CAN DB) for anomaly injection
4.Continuous multi-ECU testing with systematic project management
5.Automatic ECU status recovery during unexpected or failure scenarios
6.Failure retest functionality for reproducing abnormal test cases
7.Custom test case generation with user-defined signal data frames and evaluation criteria
8.Support for physical and HIL (Hardware-in-the-Loop) simulation environments
9.Background test execution enabling long-term continuous testing
10.Regulatory compliance coverage for UN R155 and ISO/SAE 21434
Learn more at https://cybersectools.com/tools/autocrypt-cstp-fuzzer
AutoCrypt CSTP Fuzzer is a commercial Vulnerability Management solution. For detailed pricing information, visit https://www.autocrypt.io/products/cstp/fuzzer/ or contact AUTOCRYPT directly. View more details at https://cybersectools.com/tools/autocrypt-cstp-fuzzer
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Compare these tools and more at https://cybersectools.com/categories/vulnerability-management
AutoCrypt CSTP Fuzzer is for security teams and organizations that need Fuzzing, IOT Security, Continuous Testing, Protocol Analysis, Bluetooth. It's particularly suitable for enterprises requiring robust, commercial-grade security capabilities. Other Vulnerability Management tools can be found at https://cybersectools.com/categories/vulnerability-management
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