Cyber Resilience

CVE-2023-22268

SQLi in Adobe Robohelp Server ≤ 11.4

Published
17 November 2023
Modified
21 November 2024
Patch / advisory
CVSS Score v3.1 6.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
EPSS Score 0.012 66th percentile
Risk Priority 53 floored blend · peak EPSS

Summary

CVE-2023-22268 is a medium-severity SQL Injection (CWE-89) vulnerability in Adobe Robohelp Server. Its CVSS base score is 6.5 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 34% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

Adobe RoboHelp Server versions 11.4 and earlier are affected by an Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability that could lead to information disclosure by an low-privileged authenticated attacker. Exploitation of this issue does…

more

not require user interaction.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-22275Same product: Adobe Robohelp Server
CVE-2023-23459Same product: Microsoft Windows
CVE-2019-13373Same product: Microsoft Windows
CVE-2023-3864Same product: Microsoft Windows
CVE-2023-25839Same product: Microsoft Windows
CVE-2024-9194Same product: Microsoft Windows
CVE-2023-38249Same vendor: Adobe
CVE-2023-38221Same vendor: Adobe
CVE-2023-38250Same vendor: Adobe
CVE-2023-4661Same vendor: Adobe

Affected Assets

adobe
robohelp server
≤ 11.4

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V6.2.5

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

addresses: CWE-89

Penetration testing uses SQL injection payloads against database interfaces, identifying and supporting fixes for SQL injection weaknesses.

addresses: CWE-89

Validates query inputs to prevent SQL syntax or command manipulation.

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.

PR.PS-06 mostly match
prevents

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.

PR.AT-02 partial match
prevents

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.

finds

The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.

prevents

Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.

prevents

Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.

prevents

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.

prevents

Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.

References