Cyber Resilience

CVE-2024-7472

Lunary 1.2.26

Public PoC
Published
29 October 2024
Modified
15 October 2025
Patch / advisory
View advisory
CVSS Score v3.1 6.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:N
EPSS Score 0.0043 36th percentile
Risk Priority 50 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2024-7472 is a medium-severity CRLF Injection (CWE-93) vulnerability in Lunary Lunary. Its CVSS base score is 6.5 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 36th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.

The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

A05:2025 Injection

EU & UK References

Vulnerability Data

lunary-ai/lunary v1.2.26 contains an email injection vulnerability in the Send email verification API (/v1/users/send-verification) and Sign up API (/auth/signup). An unauthenticated attacker can inject data into outgoing emails by bypassing the extractFirstName function using a different whitespace character (e.g., \xa0).…

more

This vulnerability can be exploited to conduct phishing attacks, damage the application's brand, cause legal and compliance issues, and result in financial impact due to unauthorized email usage.

CWE(s)
CWE-93CWE-74

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
Other ATLAS/OWASP Terms
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Lunary.ai is an open-source LLM observability and management platform used for monitoring and improving AI/LLM applications in enterprise settings, fitting the Enterprise AI Assistants category.

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.
attack.mitre.org →
T1221 Template Injection Stealth
Adversaries may create or modify references in user document templates to conceal malicious code or force authentication attempts.
attack.mitre.org →
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
attack.mitre.org →
T1674 Input Injection Execution
Adversaries may simulate keystrokes on a victim’s computer by various means to perform any type of action on behalf of the user, such as launching the command interpreter using keyboard shortcuts, typing an inline script to be executed,…
attack.mitre.org →
T1059 Command and Scripting Interpreter Execution
Adversaries may abuse command and script interpreters to execute commands, scripts, or binaries.
attack.mitre.org →
T1059.001 PowerShell Execution
Adversaries may abuse PowerShell commands and scripts for execution.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-32786Shared CWE-74
CVE-2023-0040Shared CWE-74, CWE-93
CVE-2023-26138Shared CWE-74, CWE-93
CVE-2023-38896Shared CWE-74
CVE-2024-5193Shared CWE-74, CWE-93
CVE-2023-39659Shared CWE-74
CVE-2023-29374Shared CWE-74
CVE-2023-26148Shared CWE-74, CWE-93
CVE-2024-45302Shared CWE-74, CWE-93
CVE-2023-23936Shared CWE-74, CWE-93

Affected Assets

lunary
lunary
1.2.26

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)
  • V1.2.1
  • V1.2.3
  • V1.2.5
  • V1.2.8

Mitigating Controls (NIST 800-53 r5) AI

SI-10 Information Input Validation good match

Input validation directly stops untrusted data containing CRLF sequences from reaching the component that treats CRLF as a delimiter.

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 require input neutralization and validation to block CRLF injection.

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.

A.8.29 Security testing in development and acceptance mostly match
finds

Security testing can detect CRLF flaws but does not itself implement the neutralization.

A.8.15 Logging partial match
finds

Logging supports detection of injection attempts but does not prevent the weakness.

A.8.16 Monitoring activities partial match
finds

Monitoring activities can identify active injection attacks after they occur.

A.8.25 Secure development life cycle partial match
prevents

Secure development lifecycle mandates input validation and output encoding that directly prevents CRLF injection.

A.8.26 Application security requirements partial match
prevents

Application security requirements include rules for neutralizing special characters such as CRLF in inputs.

A.8.27 Secure system architecture and engineering principles partial match
prevents

Secure architecture principles encourage safe handling of untrusted data but do not prescribe specific CRLF controls.

References