A11:2021 – Next Steps
By design, the OWASP Top 10 is innately limited to the ten most significant risks. Every OWASP Top 10 has “on the cusp” risks considered at length for inclusion, but in the end, they didn’t make it. No matter how we tried to interpret or twist the data, the other risks were more prevalent and impactful.
Organizations working towards a mature appsec program or security consultancies or tool vendors wishing to expand coverage for their offerings, the following four issues are well worth the effort to identify and remediate.
Code Quality issues
| CWEs Mapped | Max Incidence Rate | Avg Incidence Rate | Avg Weighted Exploit | Avg Weighted Impact | Max Coverage | Avg Coverage | Total Occurrences | Total CVEs |
|---|---|---|---|---|---|---|---|---|
| 38 | 49.46% | 2.22% | 7.1 | 6.7 | 60.85% | 23.42% | 101736 | 7564 |
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Description. Code quality issues include known security defects or patterns, reusing variables for multiple purposes, exposure of sensitive information in debugging output, off-by-one errors, time of check/time of use (TOCTOU) race conditions, unsigned or signed conversion errors, use after free, and more. The hallmark of this section is that they can usually be identified with stringent compiler flags, static code analysis tools, and linter IDE plugins. Modern languages by design eliminated many of these issues, such as Rust’s memory ownership and borrowing concept, Rust’s threading design, and Go’s strict typing and bounds checking.
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How to prevent. Enable and use your editor and language’s static code analysis options. Consider using a static code analysis tool. Consider if it might be possible to use or migrate to a language or framework that eliminates bug classes, such as Rust or Go.
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Example attack scenarios. An attacker might obtain or update sensitive information by exploiting a race condition using a statically shared variable across multiple threads.
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References
Denial of Service
| CWEs Mapped | Max Incidence Rate | Avg Incidence Rate | Avg Weighted Exploit | Avg Weighted Impact | Max Coverage | Avg Coverage | Total Occurrences | Total CVEs |
|---|---|---|---|---|---|---|---|---|
| 8 | 17.54% | 4.89% | 8.3 | 5.9 | 79.58% | 33.26% | 66985 | 973 |
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Description. Denial of service is always possible given sufficient resources. However, design and coding practices have a significant bearing on the magnitude of the denial of service. Suppose anyone with the link can access a large file, or a computationally expensive transaction occurs on every page. In that case, denial of service requires less effort to conduct.
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How to prevent. Performance test code for CPU, I/O, and memory usage, re-architect, optimize, or cache expensive operations. Consider access controls for larger objects to ensure that only authorized individuals can access huge files or objects or serve them by an edge caching network.
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Example attack scenarios. An attacker might determine that an operation takes 5-10 seconds to complete. When running four concurrent threads, the server seems to stop responding. The attacker uses 1000 threads and takes the entire system offline.
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References
Memory Management Errors
| CWEs Mapped | Max Incidence Rate | Avg Incidence Rate | Avg Weighted Exploit | Avg Weighted Impact | Max Coverage | Avg Coverage | Total Occurrences | Total CVEs |
|---|---|---|---|---|---|---|---|---|
| 14 | 7.03% | 1.16% | 6.7 | 8.1 | 56.06% | 31.74% | 26576 | 16184 |
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Description. Web applications tend to be written in managed memory languages, such as Java, .NET, or node.js (JavaScript or TypeScript). However, these languages are written in systems languages that have memory management issues, such as buffer or heap overflows, use after free, integer overflows, and more. There have been many sandbox escapes over the years that prove that just because the web application language is nominally memory “safe,” the foundations are not.
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How to prevent. Many modern APIs are now written in memory-safe languages such as Rust or Go. In the case of Rust, memory safety is a crucial feature of the language. For existing code, the use of strict compiler flags, strong typing, static code analysis, and fuzz testing can be beneficial in identifying memory leaks, memory, and array overruns, and more.
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Example attack scenarios. Buffer and heap overflows have been a mainstay of attackers over the years. The attacker sends data to a program, which it stores in an undersized stack buffer. The result is that information on the call stack is overwritten, including the function’s return pointer. The data sets the value of the return pointer so that when the function returns, it transfers control to malicious code contained in the attacker’s data.
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References