Wednesday, February 28, 2024

Multiple vulnerabilities in Adobe Acrobat Reader could lead to remote code execution

Multiple vulnerabilities in Adobe Acrobat Reader could lead to remote code execution

Cisco Talos has disclosed more than 30 vulnerabilities in February, including seven in Adobe Acrobat Reader, one of the most popular PDF editing and reading software currently available. 

Adversaries could exploit these vulnerabilities to trigger the reuse of a previously freed object, thus causing memory corruption and potentially arbitrary code execution on the targeted machine.  

Other potential code execution vulnerabilities are also present in Weston Embedded ┬ÁC/HTTP-server, a web server component in Weston Embedded's in-house operating system and an open-source library that processes several types of potentially sensitive medical tests.  

For Snort coverage that can detect the exploitation of these vulnerabilities, download the latest rule sets from Snort.org, and our latest Vulnerability Advisories are always posted on Talos Intelligence’s website.  

Multiple vulnerabilities in Adobe Acrobat Reader 

Discovered by KPC of Cisco Talos. 

Adobe Acrobat Reader contains multiple vulnerabilities that could lead to remote code execution if exploited correctly. Acrobat is known for being one of the most popular PDF readers available and allows users to fill out, edit and share PDFs. 

TALOS-2023-1905 (CVE-2024-20735), TALOS-2023-1908 (CVE-2024-20747) and TALOS-2023-1910 (CVE-2024-20749) are all out-of-bounds read vulnerabilities that could lead to memory corruption, and eventually arbitrary code execution. TALOS-2023-1909 (CVE-2024-20748) also can lead to an out-of-bounds read, but in this case, could lead to the disclosure of sensitive information about the processes running in the software that could aid an adversary in the exploitation of other vulnerabilities or to bypass detection. 

TALOS-2023-1901 (CVE-2024-20731), TALOS-2023-1890 (CVE-2024-20729) and TALOS-2023-1906 (CVE-2024-20730) can also lead to arbitrary code execution, but in this case, the vulnerability is caused by a buffer overflow.  

An adversary can exploit all the aforementioned vulnerabilities by tricking the targeted user into opening a specially crafted PDF file. Usually, these come in the form of attachments or download links on phishing emails or other social engineering tactics. 

Open-source library used in medical tests vulnerable to code execution 

Discovered by Lilith >_>. 

Talos researchers discovered multiple arbitrary code execution vulnerabilities in Libbiosig, an open-source library that processes various types of medical signal data, such as for tracking patient’s respiration levels, or measuring an electrocardiogram (ECG). The library produces the information in a way that is useable in different file formats.  

An attacker could provide a specially crafted, malicious file to exploit TALOS-2024-1918 (CVE-2024-23305), TALOS-2024-1921 (CVE-2024-21812), TALOS-2024-1922 (CVE-2024-23313) and TALOS-2024-1925 (CVE-2024-23606), which causes an out-of-bounds write. An attacker could then leverage that to execute arbitrary code on the targeted device.  

TALOS-2024-1920 (CVE-2024-21795) and TALOS-2024-1923 (CVE-2024-23310) work in the same way, but in this case, cause a heap-based buffer overflow and use-after-free condition, respectively. 

Two other vulnerabilities, TALOS-2024-1917 (CVE-2024-22097) and TALOS-2024-1919 (CVE-2024-23809), are double-free vulnerabilities that can also lead to arbitrary code execution.  

All the vulnerabilities Talos found in Libbiosig are considered critical, with a CVSS score of 9.8 out of 10. 

Use-after-free vulnerability in Imaging Data Commons libdicom 

Discovered by Dimitrios Tatsis. 

A use-after-free vulnerability (TALOS-2024-1931/CVE CVE-2024-24793, CVE-2024-24794) exists in Imaging Data Commons libdicom, causing the premature freeing of memory that is used later.  

Libdicom is a C library and a set of command-line tools for reading DICOM WSI files, commonly used in the medical field to store and transmit files. It’s commonly used in doctor’s offices, health systems and hospitals.  

An adversary could exploit this vulnerability by forcing the targeted application to process a malicious DICOM image, potentially allowing them to later cause memory corruption on the application and possibly arbitrary code execution.  

Arbitrary code execution, denial-of-service vulnerabilities in Weston Embedded server 

Discovered by Kelly Patterson. 

A critical heap-based buffer overflow vulnerability in the Weston Embedded uC-HTTP server could lead to arbitrary code execution. TALOS-2023-1843 (CVE-2023-45318) exists in the web server component of Weston’s uCOS real-time operating system.  

The overflow occurs when parsing the protocol version of an HTTP request if the adversary sends a malicious packet to the targeted machine. TALOS-2023-1843 has a maximum severity score of 10.  

The server also contains two other vulnerabilities — TALOS-2023-1828 (CVE-2023-39540, CVE-2023-39541) and TALOS-2023-1829 (CVE-2023-38562). 

TALOS-2023-1828 is a double-free vulnerability, which could also lead to code execution, while TALOS-2023-1829 could allow an adversary to cause a denial of service on the targeted device. 

5 heap-based buffer overflow vulnerabilities in implementation of LLaMA 

Discovered by Francesco Benvenuto. 

Talos discovered multiple heap-based buffer overflows in llama.cpp that could lead to code execution on the targeted machine.  

LLaMA.cpp is a project written in C/C++ that provides inference for Large Language Models (LLMs). It supports a wide variety of hardware and platforms. Besides inference, it can also be used for quantizing models and provides Python bindings for simpler integration with more complex projects. For example, it can be used to create an AI assistant like ChatGPT. LLaMA.cpp also supports GGUF, a file format for storing LLMs that focuses on extensibility and compatibility. 

LLaMA.cpp’s GitHub page says its goal is to provide users with an “LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware — locally and in the cloud.” 

An adversary could exploit the following vulnerabilities if they provide a specially crafted .gguf file, the file type commonly used to store language models for inference: TALOS-2024-1912 (CVE-2024-21825), TALOS-2024-1913 (CVE-2024-23496), TALOS-2024-1914 (CVE-2024-21802), TALOS-2024-1915 (CVE-2024-21836) and TALOS-2024-1916 (CVE-2024-23605). 



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