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CVE-2026-28811 – Information Disclosure (Debug Messages Revealing Unnecessary Information) – Apache

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Summary

CVE-2026-28811 is an information-disclosure vulnerability in Apache JSPWiki in which debug messages reveal unnecessary information through error handling. All releases up to and including version 2.12.3 are affected. It carries a CVSS v3.1 base score of 7.5 (High), reflecting a network-exploitable, confidentiality-only impact.

Technical details

  • Root cause: Error handling reveals unnecessary information via debug messages (CWE-1295), exposing sensitive details in output.
  • Trigger conditions: An unauthenticated request that causes JSPWiki to return error/debug output can surface the exposed information.
  • Attack vector: Network — no privileges and no user interaction required.
  • Impact: Disclosure of sensitive information (high confidentiality impact); no impact to integrity or availability.

Affected software

  • Apache JSPWiki: all releases up to and including 2.12.3.

Severity

  • CVSS v3.1 base score: 7.5 (High)
  • Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Mitigation and recommended actions

  • Immediate: Upgrade to Apache JSPWiki 2.12.4 or later, which resolves this issue.
  • If no patch can be applied: Restrict network exposure of the JSPWiki instance and limit access to trusted networks until the upgrade can be completed. No vendor-documented standalone workaround is available.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Raw response body: <html xmlns:jspwiki=
  • URL of a loaded script: jspwiki
  • Asset URL: wiki.jsp

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How IONIX’s External Exposure Management Platform Detects and Validates
Zero-Days to Shrink MTTR

1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

Execute Exploit Validations

By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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