libigl 2.4.0/2.5.0 OFF File readOFF.cpp stack-based overflow
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.9 | $0-$5k | 0.00 |
Summary
A vulnerability has been found in libigl 2.4.0/2.5.0 and classified as critical. This affects an unknown function of the file readOFF.cpp of the component OFF File Handler. This manipulation causes stack-based overflow. This vulnerability is registered as CVE-2023-35951. Remote exploitation of the attack is possible. No exploit is available.
Details
A vulnerability has been found in libigl 2.4.0/2.5.0 and classified as critical. Affected by this vulnerability is an unknown part of the file readOFF.cpp of the component OFF File Handler. The manipulation with an unknown input leads to a stack-based overflow vulnerability. The CWE definition for the vulnerability is CWE-121. A stack-based buffer overflow condition is a condition where the buffer being overwritten is allocated on the stack (i.e., is a local variable or, rarely, a parameter to a function). As an impact it is known to affect confidentiality, integrity, and availability. The summary by CVE is:
Multiple stack-based buffer overflow vulnerabilities exist in the readOFF.cpp functionality of libigl v2.4.0. A specially-crafted .off file can lead to a buffer overflow. An attacker can arbitrary code execution to trigger these vulnerabilities.This vulnerability exists within the code responsible for parsing geometric vertices of an OFF file.
It is possible to read the advisory at talosintelligence.com. This vulnerability is known as CVE-2023-35951 since 06/20/2023. The exploitation appears to be easy. The attack can be launched remotely. The exploitation doesn't need any form of authentication. It demands that the victim is doing some kind of user interaction. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 02/12/2025).
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 7.0VulDB Meta Temp Score: 6.9
VulDB Base Score: 6.3
VulDB Temp Score: 6.1
VulDB Vector: 🔍
VulDB Reliability: 🔍
CNA Base Score: 7.8
CNA Vector (Talos): 🔍
CVSSv2
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
Exploiting
Class: Stack-based overflowCWE: CWE-121 / CWE-119
CAPEC: 🔍
ATT&CK: 🔍
Physical: Partially
Local: Yes
Remote: Yes
Availability: 🔍
Status: Not defined
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔍
Timeline
06/20/2023 🔍05/28/2024 🔍
05/28/2024 🔍
03/27/2025 🔍
Sources
Advisory: TALOS-2023-1784Status: Not defined
CVE: CVE-2023-35951 (🔍)
GCVE (CVE): GCVE-0-2023-35951
GCVE (VulDB): GCVE-100-266395
Entry
Created: 05/28/2024 16:40Updated: 03/27/2025 19:44
Changes: 05/28/2024 16:40 (63), 02/12/2025 17:20 (2), 03/27/2025 19:44 (3)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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