Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:LCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-21628 is a critical-severity SQL Injection (CWE-89) vulnerability in Chatwoot Chatwoot. Its CVSS base score is 9.1 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 49th 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 SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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-2025-21628 is a SQL injection vulnerability (CWE-89) in Chatwoot, an open-source customer engagement suite. Versions prior to 3.16.0 are affected, specifically the conversation and contact filters endpoints. These endpoints fail to sanitize the query_operator input received from the frontend or API, enabling attackers to inject arbitrary SQL by appending a tautological WHERE clause to the filter query. The vulnerability was published on 2025-01-09 and carries a CVSS v3.1 base score of 9.1 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:L).
Any authenticated actor with low privileges can exploit this issue remotely over the network, requiring low complexity and no user interaction. Exploitation allows execution of arbitrary SQL queries within the filter context, resulting in scope-changed impacts: high confidentiality loss (e.g., data exfiltration), low integrity modification, and low availability disruption.
Chatwoot patched the vulnerability in version 3.16.0. Mitigation details are available in the security advisory at https://github.com/chatwoot/chatwoot/security/advisories/GHSA-g8f9-hh83-rcq9 and the fixing commit at https://github.com/chatwoot/chatwoot/commit/b34dac7bbe3c910186083b680e51aad5ea60b44b.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-2577
Vulnerability Data
Chatwoot is a customer engagement suite. Prior to 3.16.0, conversation and contact filters endpoints did not sanitize the input of query_operator passed from the frontend or the API. This provided any actor who is authenticated, an attack vector to run…
more
arbitrary SQL within the filter query by adding a tautological WHERE clause. This issue is patched with v3.16.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 target injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.