VulnerabilityPublished 29 Oct 2024Updated 15 Oct 2025

CVE-2024-7472: lunary-ai/lunary · CRLF Injection

Affects lunary-ai/lunarySource CVE Database V5

Not known to be exploited · CVSS 5.3 Medium.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.0 base score

5.3Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.4% chance of exploitation in 30 days – higher than 39% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: Low
Integrity: None
Availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 29 Oct 2024
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Affected software

Product	Affected	Fixed in
GitHublunary-ai/lunary`pkg:github/lunary-ai/lunary`Other threats	Affected =1.2.26	Fixed in–

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AI-suggested

Organizations should immediately review and update lunary-ai/lunary to a patched version once available. In the absence of a patch, implement strict input validation and sanitization on all user-supplied data, especially focusing on whitespace normalization to prevent bypasses using non-standard characters like \xa0. Employ output encoding techniques to neutralize CRLF sequences before including user data in email headers or bodies. Monitor outgoing emails for anomalous content that may indicate injection attempts. Implement email security controls such as SPF, DKIM, and DMARC to reduce the impact of phishing emails. Conduct user awareness training to recognize phishing attempts originating from legitimate-looking emails. Additionally, audit logs for suspicious API usage patterns and consider rate limiting or CAPTCHA challenges on the affected endpoints to reduce automated exploitation risk.

Description

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). 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.

Impact

For European organizations, this vulnerability poses a risk of phishing campaigns that can compromise user credentials or sensitive data, undermining trust in digital services. The injection of malicious content into verification or signup emails can lead to widespread user deception, potentially affecting customer retention and brand integrity. Legal and compliance risks are significant in Europe due to stringent regulations such as GDPR and ePrivacy Directive, which mandate secure handling of personal data and communication. Unauthorized email usage could result in regulatory fines and damage to corporate reputation. Financial impacts may arise from incident response costs, remediation efforts, and potential fraud resulting from successful phishing attacks. Organizations relying on lunary-ai/lunary for user authentication or communication should consider this vulnerability a moderate threat that could disrupt secure user onboarding and verification processes.

CWE-93Improper Neutralization of CRLF Sequences ('CRLF Injection') (opens in a new tab)
Base weaknessOWASP Top 10 A05:2025: Injection (opens in a new tab)
The product uses CRLF (carriage return line feeds) as a special element, e.g. to separate lines or records, but it does not neutralize or incorrectly neutralizes CRLF sequences from inputs.
Implementation
Avoid using CRLF as a special sequence.
Implementation
Appropriately filter or quote CRLF sequences in user-controlled input.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Deserialization (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V1.5.1 (opens in a new tab)
Attack patterns
Command Delimiters (opens in a new tab)
Web Server Logs Tampering (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 15 Oct 2025, 13:35 UTC

WeaknessesCWE-93

Technical details

References

Community reviews

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