Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/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-2026-25236 is a medium-severity SQL Injection (CWE-89) vulnerability in Pear Pearweb. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 18th 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-2026-25236 is a SQL injection vulnerability (CWE-89) in PEAR, a framework and distribution system for reusable PHP components. The flaw affects versions prior to 1.33.0 and stems from unsafe literal substitution in karma queries when handling IN (...) lists, enabling malicious SQL code execution. Published on 2026-02-03, it carries a CVSS v3.1 base score of 9.8, indicating critical severity due to its network accessibility, low attack complexity, lack of required privileges or user interaction, and high impacts on confidentiality, integrity, and availability.
Unauthenticated attackers with network access can exploit this vulnerability remotely by crafting malicious inputs for karma queries. Successful exploitation allows arbitrary SQL execution, potentially leading to full compromise of the database, including data exfiltration, modification, or deletion, as well as potential server takeover depending on the environment and privileges of the database user.
The GitHub Security Advisory (GHSA-95mc-p966-c29f) confirms the issue and states that it has been patched in PEAR version 1.33.0. Security practitioners should upgrade to this version or later to mitigate the risk, and review any custom karma query implementations for similar unsafe substitution patterns.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5199
Vulnerability Data
PEAR is a framework and distribution system for reusable PHP components. Prior to version 1.33.0, a SQL injection risk exists in karma queries due to unsafe literal substitution for an IN (...) list. This issue has been patched in version…
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1.33.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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.