Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-27934 is a high-severity Insertion of Sensitive Information Into Sent Data (CWE-201) vulnerability in Discourse Discourse. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Network Sniffing (T1040); ranked at the 17th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-27934 is an information disclosure vulnerability in Discourse, an open-source discussion platform. It stems from a lack of visibility checks in a user action API endpoint, which allows unauthorized users to access the title and post excerpt of otherwise restricted content. The issue affects all versions of Discourse prior to 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, and is classified under CWE-201 (Exposure of Sensitive Information to an Unauthorized Actor) with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).
The vulnerability can be exploited remotely over the network by any unauthenticated attacker with low complexity and no user interaction required. Successful exploitation enables the attacker to disclose sensitive titles and post excerpts that should not be visible to them, potentially revealing private or moderated discussion content across the platform.
According to the advisory, Discourse has patched the vulnerability in versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2. No known workarounds are available. Security practitioners should prioritize upgrading affected installations. For full details, refer to the GitHub Security Advisory at https://github.com/discourse/discourse/security/advisories/GHSA-824f-66wh-xx3g.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-13196
Vulnerability Data
Discourse is an open-source discussion platform. Versions prior to 2026.3.0-latest.1, 2026.2.1, and 2026.1.2 have a lack of visibility checks with a user action API endpoint that results in disclosure of the title and post excerpt to unauthorized users, leading to…
more
information disclosure. Versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2 contain a patch. No known workarounds are available.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
- 1 hardening rule · 1 OS baseline
V14.2.3
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces policy-based information flow rules that block transmission of sensitive data to unauthorized actors.
Enforces authorizations on logical access so that sensitive data is not released to unauthorized recipients.
Requires validation of outbound information to ensure sensitive content is not disclosed in responses or messages.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent insertion of sensitive data into application outputs and messages.
Monitoring runtime data flows and outputs can detect sensitive data being transmitted.
Protecting data-in-transit can include filtering or encrypting to avoid exposing sensitive content.
Protecting data-in-use includes removing confidential values before they are processed or sent.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Data-masking techniques can prevent sensitive values from appearing in transmitted payloads.
Classification identifies sensitive data so it is not inadvertently transmitted.
Labelling makes sensitive data visible to developers and prevents accidental inclusion in outbound messages.
Information-transfer rules directly govern what data may be sent to external parties.
PII-protection requirements reduce the chance of sending personal data to unauthorized recipients.
DLP controls inspect and block outbound flows that contain sensitive information.