Key Takeaways:
- Not All Vulnerabilities Are Real Threats: Reachability analysis helps security teams focus on exploitable risks rather than wasting time on false positives.
- Traditional SCA Tools Lack Context: SCA tools flag all vulnerable dependencies without verifying their reachability, leading to unnecessary remediation.
- AI-Driven Reachability with CPGs: Code Property Graphs (CPGs) offer a more precise assessment. They combine dataflow, control flow, and dependencies to detect real threats and reduce false positives.
What is Reachability in Security Analysis?
Reachability in security analysis determines whether a vulnerability in an upstream dependency can be exploited in a downstream application. Not all vulnerabilities are a real threat just because a dependency has a known issue doesn’t mean the affected code is reachable or actively used in your application. Traditional Software Composition Analysis (SCA) tools often flag every vulnerable dependency without considering whether that code path is executed, leading to excessive false positives and unnecessary remediation work.
Understanding reachability helps security teams prioritize what matters most. By focusing on truly exploitable vulnerabilities, teams can reduce wasted effort and concentrate on addressing actual risks. This targeted approach makes remediation efforts more efficient and improves the application’s overall security posture.
Different Approaches to Reachability Analysis
Traditional Software Composition Analysis (SCA)
Traditional Software Composition Analysis (SCA) tools scan software dependencies and flag vulnerabilities by matching them with known issues in vulnerability databases like the National Vulnerability Database (NVD). These tools analyze dependency files and report any library version with a known vulnerability, providing developers with a list of affected packages. This method helps identify which dependencies might introduce risk at a high level.
The main drawback is that it doesn’t verify whether the application uses the vulnerable code. SCA tools treat all vulnerabilities equally important, leading to large volumes of false positives. Since many of these flagged vulnerabilities don’t have an execution path that reaches the core application logic, security teams end up spending time on issues that aren’t exploitable, slowing down remediation efforts and development workflows.
Probabilistic (Approximate) Dataflow Analysis
Probabilistic or approximate dataflow analysis improves on traditional SCA by using heuristics and AI models to estimate the likelihood that a vulnerability can be exploited. This approach analyzes dataflow patterns and application structure to provide a more prioritized view of potential risks. Focusing on probabilities offers better context than simple dependency scanning and helps teams focus on vulnerabilities that are more likely to be relevant.
However, it lacks definitive confirmation of whether the vulnerable code is reachable. While this method reduces the noise compared to traditional SCA, it still relies on estimation rather than actual execution path validation. This can result in uncertainty, leaving security teams to decide whether to address or deprioritize specific issues.
Definitive (Precise) Dataflow Analysis
Definitive dataflow analysis tracks execution paths within the application to determine if a vulnerability can be triggered. This method doesn’t rely on probabilities or heuristics—it focuses on real execution flow, providing clear answers about whether a vulnerability is exploitable. It offers the most reliable and actionable insights by mapping out how data flows through the code and identifying where vulnerable code is executed.
The precision of this approach significantly reduces false positives, helping security teams focus only on threats that matter. This means less time spent on non-issues and more targeted remediation efforts. For organizations dealing with complex applications and dependencies, definitive dataflow analysis offers the most accurate and effective way to assess reachability and prioritize security work.
How Qwiet AI Enhances Reachability Analysis
Qwiet AI uses Code Property Graphs (CPGs) to combine data, control flow, and dependency information into a single, unified view, allowing security teams to see how vulnerabilities could be exploited across the application.
With AI-powered precision, the platform applies machine learning to distinguish between theoretical and real, exploitable risks, reducing the noise caused by false positives. This means developers can focus on addressing actual threats instead of being overwhelmed by irrelevant alerts.
Seamless integration into CI/CD pipelines provides real-time security insights, helping teams catch and remediate vulnerabilities before they reach production without slowing down development workflows.
Conclusion
Not all vulnerabilities are real threats, so accurate reachability analysis matters. Traditional SCA tools generate excessive false positives, wasting time on non-exploitable risks. AI-driven models powered by Code Property Graphs (CPGs) provide precise assessments by identifying exploitable vulnerabilities. This helps security teams prioritize and remediate more effectively. Book a demo today to see how Qwiet AI can streamline your security strategy.
FAQ
1. What is reachability in security analysis?
Reachability analysis determines whether a vulnerability in an upstream dependency can be exploited in the downstream application. By focusing on code paths that can execute the vulnerable code, it helps teams identify security risks.
2. Why do traditional SCA tools generate false positives?
Traditional SCA tools flag vulnerabilities based solely on a vulnerable dependency without analyzing whether that code is executed. This leads to false positives because many flagged vulnerabilities are not reachable or exploitable in the application.
3. What is the difference between probabilistic and definitive dataflow analysis?
Probabilistic dataflow analysis uses heuristics and AI to estimate the likelihood that a vulnerability is reachable. In contrast, definitive dataflow analysis tracks execution paths to confirm whether the vulnerable code can be triggered. Definitive analysis is more accurate and reduces false positives.
4. How does Qwiet AI enhance reachability analysis?
Qwiet AI uses Code Property Graphs (CPGs) to provide a full security view by combining dataflow, control flow, and dependency information. It applies machine learning to differentiate theoretical risks from real ones, reducing false positives and giving security teams actionable insights.