CVE-2023-29824: SciPy Use-After-Free Vulnerability

CVE-2023-29824 Overview

A use-after-free vulnerability was discovered in the Py_FindObjects() function in SciPy, a popular open-source Python library used for scientific and technical computing. This memory corruption flaw affects SciPy versions prior to 1.8.0 and could potentially allow attackers to execute arbitrary code or cause denial of service conditions. It is important to note that both the vendor and the original discoverer have indicated that this is not considered a security issue in typical usage scenarios.

Critical Impact

Use-after-free vulnerability in a widely-deployed scientific computing library that could potentially lead to memory corruption, arbitrary code execution, or application crashes in affected environments.

Affected Products

• SciPy versions prior to 1.8.0
• Applications and systems utilizing vulnerable SciPy library versions
• Scientific computing environments with outdated SciPy installations

Discovery Timeline

• 2023-07-06 - CVE-2023-29824 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2023-29824

Vulnerability Analysis

This vulnerability is classified as CWE-416 (Use After Free), a type of memory corruption vulnerability that occurs when a program continues to reference memory after it has been freed. In the context of the Py_FindObjects() function within SciPy, this flaw arises from improper memory management where previously deallocated memory is accessed, potentially leading to undefined behavior.

Use-after-free vulnerabilities are particularly dangerous because they can allow attackers to manipulate memory in ways that lead to arbitrary code execution. When memory is freed and then reallocated for a different purpose, accessing the original reference can result in reading or writing to memory that now contains different data or code pointers.

The vulnerability exists in the core functionality of the Py_FindObjects() function, which is part of SciPy's internal operations. While the vendor has disputed the security implications in typical deployment scenarios, the underlying memory safety issue represents a code quality concern that was addressed in version 1.8.0.

Root Cause

The root cause of this vulnerability lies in improper memory lifecycle management within the Py_FindObjects() function. The code fails to properly track memory allocation state, leading to a scenario where a pointer to freed memory is subsequently dereferenced. This type of bug typically occurs when:

1. Memory is allocated and a pointer is stored
2. The memory is freed but the pointer is not nullified
3. The dangling pointer is later accessed, referencing invalid memory

The fix implemented in GitHub Scipy Pull Request #15013 addresses this memory management issue by ensuring proper handling of object references.

Attack Vector

The vulnerability is exploitable via network-based attack vectors. An attacker could potentially craft malicious input that triggers the use-after-free condition in applications utilizing the vulnerable Py_FindObjects() function. The attack requires no user interaction or special privileges to execute.

Exploitation would involve providing specially crafted data to an application that processes it using the vulnerable SciPy function. Upon processing, the use-after-free condition could be triggered, potentially allowing:

• Memory corruption leading to arbitrary code execution
• Application crashes resulting in denial of service
• Information disclosure through memory content exposure

However, as noted by the vendor and discoverer, practical exploitation in real-world scenarios may be limited due to the specific conditions required to trigger the vulnerability.

Detection Methods for CVE-2023-29824

Indicators of Compromise

• Unexpected application crashes or segmentation faults in applications using SciPy
• Abnormal memory access patterns in processes utilizing Py_FindObjects() function
• Memory corruption errors in scientific computing applications

Detection Strategies

• Implement software composition analysis (SCA) to identify SciPy versions prior to 1.8.0 in your environment
• Monitor application logs for memory-related errors and crashes in SciPy-dependent applications
• Use runtime application self-protection (RASP) solutions to detect memory corruption attempts

Monitoring Recommendations

• Audit and inventory all systems running SciPy to identify vulnerable versions
• Implement continuous dependency scanning in CI/CD pipelines to flag outdated libraries
• Monitor for unusual process behavior or crashes in scientific computing workloads

How to Mitigate CVE-2023-29824

Immediate Actions Required

• Upgrade SciPy to version 1.8.0 or later immediately
• Audit all applications and environments for SciPy dependencies
• Prioritize patching in internet-facing applications that process untrusted input

Patch Information

The vulnerability has been addressed in SciPy version 1.8.0. The fix is documented in GitHub Scipy Pull Request #15013, which corrects the memory management issue in the Py_FindObjects() function.

Organizations should update their SciPy installations using their package manager:

# Upgrade SciPy using pip
pip install --upgrade scipy>=1.8.0

# Verify installed version
pip show scipy | grep Version

Workarounds

• If immediate patching is not possible, consider isolating applications using vulnerable SciPy versions
• Implement network segmentation to limit exposure of vulnerable systems
• Monitor affected systems for signs of exploitation while planning upgrade paths

# Check current SciPy version
python -c "import scipy; print(scipy.__version__)"

# Update SciPy to patched version
pip install scipy>=1.8.0

# Verify the update was successful
python -c "import scipy; print(scipy.__version__)"