Raw vector
CVSS:4.0/AV:N/AC:H/AT:P/PR:H/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/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-2026-34724 is a high-severity Code Injection (CWE-94) vulnerability in Zammad Zammad. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Template Injection (T1221); ranked at the 18th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the LLM/Generative AI Risks risk domain.
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-2026-34724 is a server-side template injection vulnerability in Zammad, a web-based open source helpdesk and customer support system. Affecting versions prior to 7.0.1, the flaw exists in the AI Agent component and allows remote code execution (RCE) when an attacker can control or influence the type_enrichment_data parameter, which is typically configured by high-privilege administrators. The vulnerability is associated with CWE-94 (Improper Control of Generation of Code) and CWE-1336 (Incorrect Handling of Code Blocks in Templating Engine), earning a CVSS v3.1 base score of 7.2 (AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H).
Exploitation requires high privileges (PR:H), limiting it to authenticated attackers with administrative access who can manipulate type_enrichment_data during configuration. Successful exploitation enables arbitrary code execution on the server, potentially granting full control over the affected Zammad instance with high confidentiality, integrity, and availability impacts.
The official advisory on GitHub (GHSA-fg9w-jg8f-4j94) confirms the issue is fixed in Zammad 7.0.1, recommending immediate upgrades to mitigate the vulnerability. No workarounds are specified beyond applying the patch.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-20564
Vulnerability Data
Zammad is a web based open source helpdesk/customer support system. Prior to 7.0.1, a server-side template injection vulnerability which leads to RCE via AI Agent exists. Impact is limited to environments where an attacker can control or influence type_enrichment_data (typically…
more
high-privilege administrative configuration). This vulnerability is fixed in 7.0.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.2V1.3.7V1.3.10V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Security engineering principles require use of safe templating APIs and proper escaping of external input.
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's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Security testing can detect template-injection flaws but does not itself implement neutralization controls.
Secure development life cycle mandates input validation and sanitization that directly prevents template-injection weaknesses.
Application security requirements explicitly call for neutralizing special elements in template engines.
Secure architecture principles reduce the likelihood of unsafe template processing but do not prescribe specific neutralization techniques.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.