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A vulnerability scanner can miss known issues, so a clean result does not prove that code is secure. The headline’s specific result—one scanner missed four of five CVEs and the author then reverted a fix—is the author’s report, not a reproducible benchmark: the scanner, code, CVEs, configuration, and reason for reverting the change have not been identified here. The broader warning is well supported: scanner results depend on what the tool covers, how it identifies software or code, and how it matches findings.
What does the five-CVE result establish?
It establishes only what the author reports about one run: a scanner did not flag four of five real CVEs, and the author reverted a change they had made. Without the scanner and version, target repository and revision, CVE identifiers, scan configuration, raw output, and evidence that each vulnerable condition was present in the tested state, the result cannot be independently checked or generalized to other scanners.
The type of scanner matters. Static analysis examines source code for code-level defects; dependency scanners match installed components and versions to vulnerability records; binary scanners inspect compiled software. A test of one category does not establish how another performs.
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Why can a vulnerability scanner miss a known issue?
It may not cover or recognize the software
NIST describes vulnerability scanning as comparing known-vulnerable software versions with the versions present on devices. A scanner can only detect what its coverage and detection data let it recognize. NIST recommends checking how much of the known-vulnerability set a tool covers, measuring false-negative and false-positive rates, and checking whether detection code is updated promptly. Its guidance is direct: “No test is 100 % reliable.” (NISTIR 8011, Volume 4)
Dependency records and package identifiers may not match
Dependency scanners often rely on package metadata and vulnerability records to decide whether a component and version are affected. That match can fail when an affected artifact is missing from a database, its identifier is incomplete or expressed at a different level of detail, or databases disagree about affected artifacts and versions. Code-based approaches can inspect bytecode or vulnerable constructs, but still depend on suitable vulnerability data. These matching problems are discussed in the OSS vulnerability-scanner study.
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Static analyzers perform differently across bug types and code
In a 2022 study of six static C analyzers, researchers evaluated 27 real-world projects totaling 1.15 million lines of code and containing 192 ground-truth vulnerabilities. Individual analyzers missed 47%–80% of vulnerabilities in that dataset. Those figures apply to that study’s C analyzers and benchmark; they are not estimates for all scanners or for the author’s five-CVE run. The study also cautions that performance on synthetic benchmarks may not transfer to real-world vulnerabilities. (Lipp, Banescu, and Pretschner, ISSTA 2022)
NIST’s SATE VI evaluation likewise found effectiveness varied with test cases, bug classes, and complexity. Simpler initialization errors were found more readily than intricate buffer errors; results also differed between its C and Java tracks. NIST concludes static analysis can help find real security bugs, while advising organizations to evaluate tools against their own code before production use. (NIST SP 500-341, SATE VI Report)
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Can using more than one analyzer help?
It can catch issues one tool misses, but it can also produce more findings for people to review. In the same 2022 study of six static C analyzers, combining analyzers reduced the reported miss share to 30%–69% and increased the share of functions flagged by 15 percentage points. That is a tradeoff observed on that dataset, not a guarantee that adding tools will improve results by the same amount elsewhere.
When comparing tools, run them against the same code and known-vulnerability ground truth with comparable settings and timing. Count both missed issues and extra findings, and account for the human review those findings require. The available evidence does not establish a universally best scanner.
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How should you test a scanner on your own code?
- Define what is being scanned. Record whether the test is source-code analysis, dependency/version matching, binary scanning, or another method. Capture the repository revision, build state, dependency metadata, scanner version, and configuration.
- Choose ground-truth vulnerabilities. List the CVEs or code-level defects, and document why each applies to the exact tested state. A CVE number alone does not establish that a particular component or revision is affected.
- Run the scan and preserve its output. Record when it ran, what components or files it covered, and which findings it returned. A result is meaningful only in relation to that scope and configuration.
- Measure misses and noise separately. Compare output with the ground truth to identify missed vulnerabilities, then record unrelated or duplicate findings and the review effort they create.
- Evaluate fixes independently. Validate a code change with tests and review its behavior. Treat scanner detection as one input; do not use a scan result alone to decide whether a patch is correct.
- Repeat when the code or detection data changes. Scanner coverage and vulnerability records can change over time, so preserve the date and database or rule-update context when making comparisons.
NIST’s recommendation is: “Potential users should test a tool or set of tools on their own code base before using them in production.”
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- The scanner name, version, scan category, and relevant configuration.
- The repository and revision, plus build or dependency state needed to reproduce the run.
- All five CVE identifiers and evidence that each applied to the tested state.
- The scanner’s raw output and the rule used to classify a CVE as detected or missed.
- The exact fix that was reverted and the evidence behind the rollback.
With those details, readers could distinguish a real detection failure from a scope, identification, configuration, or ground-truth mismatch. Without them, the four-of-five outcome remains an attributed report rather than a reproducible measurement.
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