Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-47117 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Humansignal Label Studio. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Label Studio, an open-source data labeling tool, is affected by an information disclosure vulnerability in all versions prior to 1.9.2post0. The application permits unauthenticated users to supply arbitrary filter expressions when querying tasks; these expressions are passed directly to Django's ORM, enabling attackers to reference sensitive model fields such as password hashes. A second issue is the presence of a hard-coded Django secret key that can be used to sign arbitrary session tokens once the hashes have been obtained.
An unauthenticated remote attacker can chain ORM filter predicates to extract sensitive column values character-by-character across all user accounts. With the leaked password hashes and the static secret key, the attacker can forge valid session cookies for any user, achieving full account takeover without requiring prior credentials or user interaction.
The vendor addressed both issues in commit f931d9d129, included in release 1.9.2post0. The GitHub Security Advisory GHSA-6hjj-gq77-j4qw explicitly states that no workarounds exist and recommends immediate upgrade.
The associated EPSS score reached 0.7064 without a documented low-to-high trajectory after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-0108
Vulnerability Data
Label Studio is an open source data labeling tool. In all current versions of Label Studio prior to 1.9.2post0, the application allows users to insecurely set filters for filtering tasks. An attacker can construct a filter chain to filter tasks…
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based on sensitive fields for all user accounts on the platform by exploiting Django's Object Relational Mapper (ORM). Since the results of query can be manipulated by the ORM filter, an attacker can leak these sensitive fields character by character. In addition, Label Studio had a hard coded secret key that an attacker can use to forge a session token of any user by exploiting this ORM Leak vulnerability to leak account password hashes. This vulnerability has been addressed in commit `f931d9d129` which is included in the 1.9.2post0 release. Users are advised to upgrade. There are no known workarounds for this vulnerability.
- CWE(s)
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
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.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.
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.5.10 only requires awareness, a topic-specific acceptable-use policy stating expected behaviours/monitoring, and procedures referencing classification and other controls; per the owner's explicit rule on labelling and similar dependency-style clauses, this does not itself prevent exposure (CWE-200) unless access/DLP/etc. act on it.
Requiring the return of all physical and electronic assets plus secure deletion of organizational data from personal devices reduces the chance that sensitive information remains accessible to departing personnel.
A.5.12 only defines and communicates a classification scheme so that protection needs can be understood; per the owner's explicit rule, labeling/classification does not itself prevent exposure and requires other controls (e.g. access enforcement) to act on it.
By requiring classification labels and metadata on all information assets, the control makes it harder for sensitive data to be inadvertently exposed because downstream systems and users can recognize and enforce handling rules based on those labels.
Requiring encryption, access controls, and recipient authentication for transfers directly reduces the chance that sensitive data reaches an unauthorized observer.
Secure delivery, protected storage, and confidentiality of allocation records limit exposure of authentication material to unauthorized observers.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.