Always test your code base for undetected vulnerabilities and bugs
Market demands often place enormous delivery pressures on developers and managers to get code rushed into production. Zealous marketing operations rarely account for the time and effort it takes to build a product that is cyber secure, including third party software.
However, a product that is security compromised in the field can negatively impact availability, reputation, new customer acquisition and share price. The number of security incidents reported every month shows there’s no shortage of malicious actors looking to exploit weak products.
Ensuring that security is built into a product throughout software development life cycles needn’t be like finding your way in the dark. Using proven, highly automated and continuous fuzz testing techniques enables developers to build confidence, and free up valuable time for other items.
1. Improve build quality
Intelligent fuzz testing increases the speed of innovation, whilst giving you and your customers some assurance that new vulnerabilities aren’t inadvertently finding their way into your products.
Fuzz to ensure robust products
Fuzz testing (a.k.a. fuzzing) is a technique used to check the quality and robustness of secure code. Fuzzing is used by many companies secure development life cycles including Microsoft, Google and Cisco. It’s supported by the UK Government’s National Cyber Security Centre (NCSC) and endorsed by US government’s National Institute of Standards and Technology (NIST). Trends in national and international standards, increasingly recommend fuzz testing as an important risk reduction technique.
How fuzz testing works
Fuzz testing is a process that involves sending invalid, unexpected or deliberately malicious test inputs to a target system, often on a massive scale. The purpose is to look for unexpected and undesirable behavior, such as information leaks, process crashes and corrupted data.
Intelligent fuzz testing is a highly effective way to find and prevent security vulnerabilities including the infamous ‘zero-days’ and does not rely on look-up data from libraries. The automated nature of fuzzing can produce tens of thousands of test cases that would be infeasible to generate, execute or analyse manually. Fuzzing therefore commonly discovers software security anomalies that other testing techniques simply fail to identify.
As software and the associated communications interfaces evolve, security flaws tend to creep in. Hackers take advantage of this. Automatic fuzzing can be integrated into secure development life cycles to continuously identify and address the underlying causes of vulnerabilities.
2. Reduce costs of development
Fix issues during production. Remedies typically become much more expensive post deployment.
What to look for in a fuzzing solution
Your fuzz testing platform should be able to:
- Create test sets that fit the time you have available and your testing goals
- Tailor the type of coverage to your priorities
- Fuzz any interface, irrespective of whether it uses common or bespoke protocols
- Collect evidence for full traceability and repeatability
- Provide dynamic indicators of interest (things to check out/fix) while the tests are running
- Automatically analyse results and provide meaningful, actionable insights
- Prioritise findings using scoring, to assess risk and make follow up more efficient
When done well, early and regular fuzz testing significantly reduces costly production stage security errors and increases customer confidence in products.
Cost and time savings can be achieved by avoiding retrospective identification of security vulnerabilities and hence the risk of having to replace vulnerable products deployed in the field.
Fuzz testing consistently shows itself to be one of the most useful things you can do to improve the quality of your code base and the overall robustness of your product. It’s one of the most effective techniques you can deploy, relative to the time and cost it takes to undertake.
To understand how fuzzing can work for you, why not talk to us.
3. Reduce risk
Early and regular fuzz testing will vastly reduce the attack surface and help you to prevent unwanted security breaches later.
About
The CyTAL team has decades of fuzz testing experience gained from working across many industry sectors including smart energy and cyber physical systems.
CyTAL’s fuzz testing solution, ProtoCrawler, is widely used by development and quality assurance teams in well-known companies and organisations.
Find out more about ProtoCrawler
Fuzzing FAQs
What is fuzzing (fuzz testing)?
Fuzzing is an automated software test technique where inputs (often invalid, random, or malformed) are fed to a target system (e.g. parser, protocol handler, API) to observe crashes, exceptions, unexpected behavior, or security vulnerabilities
Why bother using fuzzing?
Because fuzzing can uncover bugs and security issues that traditional testing or code reviews may miss. It strengthens robustness, lowers risk in deployment, and helps detect edge-case failures before they impact customers.
How does fuzzing differ from other testing methods (unit tests, static analysis)?
Fuzzing stresses the system at runtime by exploring unexpected or malformed inputs. It complements (rather than replaces) unit tests, static/dynamic analysis, formal methods, and manual review by focusing on runtime anomalies
What are the main types of fuzzing?
Mutation-based fuzzing – modify existing valid inputs.
Generation-based fuzzing – build inputs from scratch based on a format/grammar.
Coverage-guided fuzzing – use execution feedback (e.g. code coverage) to guide mutation.
Protocol-aware / grammar-based fuzzing – inject knowledge of message structure or protocol to build more targeted tests.
What is coverage-guided fuzzing and why is it powerful?
Coverage-guided fuzzing tracks which parts of the code are exercised by each test input. That feedback lets the fuzzer prioritize inputs that lead to new coverage, thus efficiently exploring deeper or rarely triggered paths.
Can fuzzing handle stateful, multi-step protocols (e.g. handshake, session setup)?
Yes, but it’s more complex. In such cases, fuzzers need to maintain or emulate a valid sequence of protocol interactions before injecting malformed parts. Protocol models, state machines, or replay frameworks may be needed.
What role does machine learning or heuristics play in modern fuzzing?
Some advanced fuzzers use ML to predict which mutations are more likely to yield new code paths or crashes. Others apply heuristics to cluster or prioritize crash outputs, reducing manual triage effort.
How are fuzzing findings prioritized?
Fuzzers often de-duplicate crash reports by stack traces or memory snapshots, classify them (buffer overflow, use-after-free, logic error, etc.), and assign severity scores. This helps developers focus first on the most critical vulnerabilities.
Is fuzzing effective for languages with memory safety (e.g. Rust, Java)?
Yes. Although memory corruption is less common, fuzzing can still uncover logic flaws, incorrect input handling, pitfalls in error handling, performance bottlenecks, or denial-of-service vulnerabilities
When in the software lifecycle should fuzzing be integrated?
Preferably early and continuously: during development, regression testing, nightly builds, or as part of CI/CD pipelines. Ongoing fuzzing helps catch regressions or new vulnerabilities introduced by changes.
What challenges or limitations should you watch out for?
High volume of tests (resource/time constraints)
False positives or low-value findings that need manual triage
Difficulty in fuzzing highly stateful or context-sensitive components
Missing preconditions or environmental setup required to trigger real vulnerabilities
Overhead of logging and trace collection
How do you assess whether a fuzzing run is “good enough”?
Metrics can include code coverage achieved, number of unique crashes, rate of new coverage growth over time, stability of results (fewer new crashes over time), and alignment with threat models or target risk areas.
What should you look for in a fuzzing tool or platform?
Key features to seek include:
Ability to generate and mutate inputs intelligently
Support for custom protocols or formats
Coverage feedback and execution monitoring
Automated crash analysis and prioritization
Traceability and evidence (logs, traces) for remediation
Integrability with CI/CD or test workflows
Scalability (parallel execution, cloud support)
How does ProtoCrawler help with protocol fuzzing?
ProtoCrawler is an intelligent fuzzing tool designed specifically for protocol security and robustness testing. It provides:
Automatic test generation and configuration tailored to your protocol interface
Detailed execution and coverage monitoring with real-time feedback
Automated analysis and prioritization of findings via scoring matrices
Protocol modeling support (for known protocols) to guide fuzzing inputs
Reporting capabilities to generate actionable insights for developers and risk owners
Which protocols does ProtoCrawler support?
ProtoCrawler supports a wide variety of IT and OT protocols. Some of the currently supported ones include ARP, ASN.1, DHCP v4/v6 (client & server), COSEM, and more. If a protocol isn’t listed, you can enquire about custom support.
Can ProtoCrawler be used for both fuzzing and functional testing?
Yes ProtoCrawler is modular and can be adapted for functional or compliance testing in addition to security fuzzing. It aligns with certification and assurance needs as well.
How does ProtoCrawler help with creating assurance evidence?
ProtoCrawler captures execution traces, test logs, coverage data, and automated reports. These artifacts provide traceable evidence of the testing performed, which can support third-party evaluations or compliance audits.
How should fuzzing fit into a broader vulnerability strategy?
Fuzzing should be one pillar of a holistic approach. Combine it with threat modeling, static/dynamic analysis, penetration testing, code reviews, and patch management. Use fuzz results to feed your vulnerability management pipeline.
Can fuzzing find zero-day vulnerabilities?
Yes. Because fuzzers inject unexpected or malformed inputs in large volume, they can uncover previously unknown vulnerabilities that weren’t anticipated during design or testing.
What next steps should I take to start fuzzing (or using ProtoCrawler)?
Identify input-handling modules (protocol parsers, API endpoints)
Model or capture valid protocol interactions
Deploy a pilot fuzzing run to gather initial coverage and findings
Review triaged issues, fix them, re-run fuzzing
Integrate fuzzing into your CI/CD or nightly pipeline
Monitor coverage trends, regressions, and new crash discovery over time
Additional information about Fuzzing
When implementing fuzzing in your development process, it’s essential to understand the various approaches available. Application fuzzing can be categorised into several distinct methodologies, each offering unique benefits of fuzzing for different scenarios.
Black box fuzzing operates without knowledge of the target system’s internal structure or source code. This approach mimics real-world attack scenarios where the fuzzer generates random input data to probe the application for weaknesses. Black box testing is particularly cost effective because it requires minimal setup and can be deployed quickly across different systems. Protocrawler excels at black box fuzzing, automatically discovering and testing network protocols without requiring access to source code or detailed protocol specifications.
White box fuzzing leverages access to source code, allowing the testing tool to create more intelligent test cases based on code paths and logic. Grey box fuzzing strikes a balance between these approaches, using limited information about the target system to guide the fuzzing process while maintaining the efficiency of black box methods.
The Role of Fuzzing Tools in Modern Security Testing
Fuzzing tools have become indispensable in security testing and quality assurance workflows. These sophisticated platforms automate the process of generating and executing test cases, dramatically reducing the time required to find bugs and security vulnerabilities. Modern fuzzing tools integrate seamlessly into the software development lifecycle, providing continuous security testing throughout the development process.
A well-configured fuzzer generates millions of test inputs, each designed to probe different aspects of your application. Protocrawler takes this automation further by intelligently learning protocol structures as it tests, creating highly targeted inputs that maximise code coverage. This automated approach is far more cost effective than manual testing, capable of discovering edge cases and unexpected behaviours that human testers might miss. The ability to run continuously means your quality assurance team can identify issues early, when they’re least expensive to fix.
Minimising False Positives in Fuzzing Campaigns
One challenge in security testing is managing false positives alerts that indicate problems where none exist. Advanced fuzzing tools employ sophisticated analysis techniques to distinguish genuine security vulnerabilities from benign anomalies. Protocrawler’s intelligent protocol analysis reduces false positives by understanding the context of responses from your target system, ensuring your team focuses on real issues rather than chasing phantom bugs.
By tuning your testing tool parameters and establishing baseline behaviours for your target system, you can significantly reduce false positives while maintaining comprehensive coverage. The development process benefits from this precision when your team can trust that fuzzing results represent genuine issues, they can prioritise remediation efforts effectively and maintain development velocity without constant interruption from spurious alerts.
Integrating Fuzzing into Software Development
Successful application fuzzing requires integration into your broader software development workflow. By incorporating security testing early in the development process, teams can identify and address security vulnerabilities before they reach production. This shift-left approach is highly cost effective, preventing the exponential cost increases associated with late-stage bug discovery.
Protocrawler supports seamless integration into CI/CD pipelines, enabling automated security testing with every build. Modern fuzzing tools support various integration points, from pre-commit hooks that validate code changes to continuous integration pipelines that automatically test each build. This automation ensures that as the fuzzer generates new test cases, they’re immediately executed against the latest code, providing rapid feedback to developers.
Benefits of Fuzzing for Quality Assurance
The benefits of fuzzing extend far beyond security testing. Quality assurance teams leverage fuzzing to discover reliability issues, performance bottlenecks, and compatibility problems. By subjecting applications to random input combinations that users might never intentionally create, fuzzing reveals unexpected behaviours that could impact user experience or system stability.
Fuzzing is particularly effective at uncovering issues in input validation, memory management, and error handling – areas where security vulnerabilities often originate. Protocrawler’s ability to automatically discover and test network protocols makes it invaluable for finding bugs in communication layers, API implementations, and IoT device interfaces. The ability to find bugs automatically, without requiring detailed test case specifications, makes fuzzing an essential addition to any testing tool portfolio.
Choosing the Right Fuzzing Approach for Your Target System
Different types of fuzzing suit different scenarios. For legacy applications where source code may be unavailable or poorly documented, black box approaches offer the fastest path to finding security vulnerabilities. For new development where quality assurance is paramount, white box fuzzing leveraging source code access can provide deeper insights and more thorough coverage.
The target system’s complexity and criticality should guide your choice of fuzzing tools and methodology. High-value applications that handle sensitive data warrant more sophisticated fuzzing approaches with extensive test cases, while less critical systems might benefit from lightweight, cost effective fuzzing that integrates easily into existing development process workflows. For network-facing applications and protocol implementations, Protocrawler provides the specialized capabilities needed to thoroughly test communication interfaces and discover protocol-level vulnerabilities.
Maximizing the Value of Random Input Testing
The power of random input generation lies in its ability to explore unexpected code paths. When a fuzzer generates random input, it’s not constrained by human assumptions about how software should be used. This freedom allows fuzzing to discover edge cases and unusual interactions that lead to unexpected behaviours, security vulnerabilities, or system crashes.
Effective random input strategies balance pure randomness with structure-aware generation. Understanding the input format expected by your target system allows the testing tool to create inputs that are more likely to reach deep code paths while still maintaining sufficient randomness to discover novel issues. Protocrawler’s adaptive learning approach automatically infers protocol structures from observed traffic, combining the thoroughness of random input testing with the efficiency of intelligent, structure-aware fuzzing. This hybrid approach maximises coverage while keeping the testing process cost effective and time-efficient.
Getting Started with Application Fuzzing
Whether you’re adding security testing to an established software development pipeline or building quality assurance processes from scratch, selecting the right fuzzing tools is critical. Consider factors like your target system’s architecture, available source code access, integration requirements, and the types of fuzzing your team needs to perform.
For organisations focused on network security, protocol testing, or IoT device security, Protocrawler offers a specialised solution designed specifically for protocol fuzzing challenges. Its black box approach means you can begin finding security vulnerabilities immediately, without the overhead of instrumenting code or creating detailed protocol specifications. By automating the discovery of unexpected behaviours and security vulnerabilities in network protocols, Protocrawler helps development process teams maintain security without slowing down release cycles.