Security Resilience

CVE-2024-12391

Binary-Husky Gpt Academic 2024-10-15

Public PoC
Published
20 March 2025
Modified
15 October 2025
CVSS Score v3 6.5
Click a component to see what it means
Raw vectorCVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS Score 0.0095 60th percentile
Risk Priority 52 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-2024-12391 is a medium-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 6.5 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique OS Credential Dumping (T1003); ranked in the top 40% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.

EU & UK References

Vulnerability Data

A vulnerability in binary-husky/gpt_academic, as of commit 310122f, allows for a Regular Expression Denial of Service (ReDoS) attack. The function '解析项目源码(手动指定和筛选源码文件类型)' permits the execution of user-provided regular expressions. Certain regular expressions can cause the Python RE engine to take exponential…

more

time to execute, leading to a Denial of Service (DoS) condition. An attacker who controls both the regular expression and the search string can exploit this vulnerability to hang the server for an arbitrary amount of time.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1003 OS Credential Dumping Credential Access
Adversaries may attempt to dump credentials to obtain account login and credential material, normally in the form of a hash or a clear text password.
T1003.003 NTDS Credential Access
Adversaries may attempt to access or create a copy of the Active Directory domain database in order to steal credential information, as well as obtain other information about domain members such as devices, users, and access rights.
T1003.006 DCSync Credential Access
Adversaries may attempt to access credentials and other sensitive information by abusing a Windows Domain Controller's application programming interface (API)(Citation: Microsoft DRSR Dec 2017) (Citation: Microsoft GetNCCChanges) (Citati…
T1005 Data from Local System Collection
Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration.
T1008 Fallback Channels Command And Control
Adversaries may use fallback or alternate communication channels if the primary channel is compromised or inaccessible in order to maintain reliable command and control and to avoid data transfer thresholds.
T1012 Query Registry Discovery
Adversaries may interact with the Windows Registry to gather information about the system, configuration, and installed software.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-12388Same product: Binary-Husky Gpt Academic
CVE-2024-10819Same product: Binary-Husky Gpt Academic
CVE-2024-12387Same product: Binary-Husky Gpt Academic
CVE-2024-12390Same product: Binary-Husky Gpt Academic
CVE-2025-10236Same product: Binary-Husky Gpt Academic
CVE-2024-10948Same product: Binary-Husky Gpt Academic
CVE-2024-10714Same product: Binary-Husky Gpt Academic
CVE-2024-12389Same product: Binary-Husky Gpt Academic
CVE-2024-10812Same product: Binary-Husky Gpt Academic
CVE-2025-0183Same product: Binary-Husky Gpt Academic

Affected Assets

binary-husky
gpt academic
2024-10-15

Mitigating Controls

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.

Development standards and tools can require safe regex construction and forbid known exponential patterns.

Denial-of-service protections limit resource exhaustion caused by expensive regex evaluation.

Input validation can constrain data that would otherwise trigger worst-case regex complexity.

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.

ID.RA-01 partial match
blocks

Vulnerability identification processes can discover ReDoS issues in existing code but do not stop their introduction.

PR.PS-06 partial match
blocks

Secure SDLC practices directly prevent inefficient regex via reviews, static analysis, and safe libraries.

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.

removes
removes
removes
removes
removes

References