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Contrast Security Report Finds AI Application-Security Scanners Agree on Just 5% of Findings

Three AI-powered AppSec scanners run on the same codebase agreed on only 5% of findings, and one scanner reproduced just 17% of its own results on repeat runs, Contrast Security's AppSec Overflow 2026 report finds.

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Overview

AI-powered application-security scanners struggle to agree even with themselves, according to a new report from Contrast Security. When three AI scanners were run against the same codebase, they agreed on just 5 percent of findings, and a single scanner run three times against identical code reproduced only 17 percent of its own results, according to Help Net Security. The finding, drawn from Contrast’s AppSec Overflow 2026 report, also appears on the company’s own report page, which lists it as “Three AI scanners, same code, 5% agreement.”

What We Know

The AppSec Overflow 2026 report draws on telemetry collected from inside hundreds of thousands of production applications and APIs, according to Help Net Security. Beyond the scanner-agreement test, the report describes a widening gap between how fast attackers exploit vulnerabilities and how fast defenders fix them:

  • Applications monitored by Contrast carry an average of 106 vulnerability findings in code written in-house, including 22 rated high or critical severity, according to Help Net Security. Of those, only 3.4 are fixed per month, and average time to fix a critical vulnerability runs 92 days, a figure independently listed on Contrast’s report page as “Exploits land in hours while patching takes 92 days.”
  • Attackers touch a running application once every 4 minutes on average, and applications face 42 confirmed viable exploit attempts a month that reach real code, according to both Help Net Security and Contrast’s report page.
  • Scanning a 2-million-line codebase with AI tools cost around $315 in API charges, but triaging the resulting findings cost around $128,000, according to Help Net Security.
  • Among CVEs on CISA’s Known Exploited Vulnerabilities list within Contrast’s dataset, 82 percent carried an EPSS exploit-prediction score of 90 percent or higher, according to Help Net Security.
  • More than 60 percent of applications see fewer than 3,000 attacks a month, while more than a quarter absorb upward of 30,000 attacks a month, according to Help Net Security.
  • Untrusted deserialization led confirmed exploit techniques in the data, followed by path traversal and method tampering, and SQL injection ranked among the top five attack techniques in every industry vertical Contrast tracked, from finance to healthcare to manufacturing, according to Help Net Security.
  • The report points to older vulnerabilities such as Spring4Shell and Log4Shell as still showing up widely in production telemetry, and cites CVE-2006-1547 and CVE-2023-38180 — both carrying a CVSS score of 7.5 with EPSS scores under 25 percent — as examples confirmed exploited in the wild despite low predicted-exploitation scores, according to Help Net Security.
  • The report also cites a broader industry pattern: HackerOne paused new submissions to the Internet Bug Bounty program, the longest-running crowdsourced vulnerability program in open source, in March 2026, and Node.js paused its own bounty program shortly afterward, citing the loss of that funding, according to Help Net Security.
  • The report cites the “Zero Day Clock,” which aggregates exploit signals from more than 83,000 CVEs, showing mean time to exploit fell from more than two years in 2018 to under one year by 2021, with the majority of exploited vulnerabilities in 2025 weaponized within three weeks, according to Help Net Security.

Jeff Williams, CTO at Contrast Security, framed the shift in stark terms: “For twenty years the discipline of AppSec has been organized around a race: find the vulnerability, decide if it matters, and fix it before somebody with bad intent finds it first. AI ended that race, and defenders lost it. We are now seeing vulnerabilities weaponized in hours while the average critical fix takes weeks or months,” he said, according to Help Net Security.

David Lindner, CISO at Contrast Security, was blunter about the scanner-agreement findings: “AI is not going to triage its way out of this problem, and we have the data to prove it,” he said, adding, “These tools disagree with each other; they disagree with themselves from one run to the next, and none of them can tell me how my application behaves when someone is actually attacking it. That is fine when AI is one input among several. It is a problem when it becomes the system of record, because that is what decides what my team works on Monday morning,” according to Help Net Security.

What We Don’t Know

Neither Contrast’s report page nor the Help Net Security coverage names the specific AI scanners tested in the three-scanner comparison, so it is not clear which commercial or open-source tools were involved or whether the low agreement rate generalizes across the broader market of AI-assisted static and dynamic analysis products. The full methodology behind the report — including exact sample sizes for the scanner-agreement test — was not detailed in either source reviewed.

Analysis

The report’s central claim is not that AI scanning tools find nothing real, but that their output is inconsistent enough — both against each other and against themselves on repeat runs — that treating any single tool’s findings as authoritative risks misdirecting a security team’s limited remediation capacity. Contrast, which sells runtime application security instrumentation, has a commercial interest in contrasting scan-time detection with what it calls real-world attack telemetry, a framing evident in Lindner’s comment that disagreement matters most when a scanner’s output becomes “the system of record” for what gets fixed.