CVE-2024-5386
Lunary ≤ 1.2.14
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-5386 is a high-severity Excessive Attack Surface (CWE-1125) vulnerability in Lunary Lunary. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 39th 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 Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to CM-7 (Least Functionality) and SC-25 (Thin Nodes) — 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-2024-5386 is an account hijacking vulnerability in lunary-ai/lunary version 1.2.2, stemming from a password reset token leak. The flaw arises due to an excessive attack surface that exposes the 'recoveryToken' parameter in server responses, enabling unauthorized access. It carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H) and maps to CWE-1125.
A low-privileged user with a 'viewer' role can exploit the vulnerability by sending a specific request to the server, which leaks a password reset token for another user's account. The attacker can then use this token to reset the target account's password without further authorization, resulting in full account takeover and privilege escalation from viewer-level access.
Patches and mitigation details are available in the fixing commit at https://github.com/lunary-ai/lunary/commit/fc7ab3d5621c18992da5dab3a2a9a8d227d42311 and the associated Huntr bounty report at https://huntr.com/bounties/602eb4a1-305d-46d6-b975-5a5d8b040ad1.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-55392
Vulnerability Data
In lunary-ai/lunary version 1.2.2, an account hijacking vulnerability exists due to a password reset token leak. A user with a 'viewer' role can exploit this vulnerability to hijack another user's account by obtaining the password reset token. The vulnerability is…
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triggered when the 'viewer' role user sends a specific request to the server, which responds with a password reset token in the 'recoveryToken' parameter. This token can then be used to reset the password of another user's account without authorization. The issue results from an excessive attack surface, allowing lower-privileged users to escalate their privileges and take over accounts.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, lunary
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 8 hardening rules · 4 OS baselines
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Mitigating Controls (NIST 800-53 r5) AI
Least Functionality directly limits the number of exposed interfaces and services, structurally preventing an excessive attack surface from being introduced.
Thin nodes enforce minimal functionality and storage, directly limiting the quantitative attack surface.
Least privilege reduces the privileges available through existing interfaces but does not remove the interfaces themselves.
Separating user and system functionality reduces reachable entry points and therefore shrinks the attack surface.
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 explicitly include attack-surface measurement and minimization during design and implementation.
Hardened baselines and configuration management directly reduce unnecessary services, ports, and features that enlarge attack surface.
Restricting execution to only permitted software prevents addition of extraneous components that increase attack surface.
Prioritization helps focus reduction efforts on high-value assets but does not itself limit the quantitative attack surface.
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.
Network segregation limits lateral exposure and therefore the effective attack surface.
Secure architecture principles explicitly call for minimizing the attack surface through least functionality.
Configuration management directly limits unnecessary services, ports and features that enlarge the attack surface.
Network security controls (firewalls, segmentation, hardening) shrink the externally reachable attack surface.
Restricting network services reduces exposed interfaces, though broader attack-surface decisions may still remain.
Secure SDLC practices include attack-surface reduction reviews during design and release.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (2 rules)
- V-248823 OL 8 must not have the telnet-server package installed. prevents CWE-1125
- V-248827 OL 8 must not have the rsh-server package installed. prevents CWE-1125
RHEL 7 (2 rules)
- V-204502 The Red Hat Enterprise Linux operating system must not have the telnet-server package installed. prevents CWE-1125
- V-204442 The Red Hat Enterprise Linux operating system must not have the rsh-server package installed. prevents CWE-1125
RHEL 8 (1 rule)
- V-230487 RHEL 8 must not have the telnet-server package installed. prevents CWE-1125