CVE-2022-4398 in radare2
Summary
by MITRE • 12/10/2022
Integer Overflow or Wraparound in GitHub repository radareorg/radare2 prior to 5.8.0.
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Analysis
by VulDB Data Team • 08/29/2025
The vulnerability identified as CVE-2022-4398 represents a critical integer overflow or wraparound issue discovered in the radare2 reverse engineering framework prior to version 5.8.0. This flaw exists within the core binary analysis capabilities of the tool, specifically affecting how the software handles integer arithmetic operations during memory allocation and data processing. The vulnerability stems from insufficient input validation and boundary checking mechanisms that allow maliciously crafted input to cause integer wraparound conditions, potentially leading to memory corruption and arbitrary code execution. Radare2 is widely used by security researchers, penetration testers, and malware analysts for binary analysis, making this vulnerability particularly concerning given its potential impact across multiple security domains.
The technical implementation of this vulnerability occurs when the software processes user-supplied data that gets converted into integer values for memory allocation or loop counters. When these integers exceed their maximum representable value, they wrap around to negative or unexpectedly small values, creating buffer overflows or incorrect memory access patterns. This type of flaw falls under the Common Weakness Enumeration category CWE-190, which specifically addresses integer overflow conditions that can result in memory corruption. The vulnerability is particularly dangerous because it can be triggered through normal file processing operations, meaning that simply opening a maliciously crafted binary file could lead to exploitation. Attackers can leverage this condition to manipulate memory layout, bypass security controls, or execute arbitrary code within the context of the radare2 process.
The operational impact of CVE-2022-4398 extends significantly beyond simple memory corruption, as it affects the integrity and reliability of reverse engineering operations that security professionals depend upon. When exploited, this vulnerability could allow attackers to gain unauthorized access to sensitive analysis data, manipulate binary analysis results, or even take complete control of systems running vulnerable versions of radare2. The attack surface is particularly broad since radare2 is used in various security contexts including malware analysis, penetration testing, and digital forensics. The vulnerability's exploitation could lead to data breaches, system compromise, and disruption of critical security operations. Organizations relying on radare2 for security research and analysis face significant risk, especially those that process untrusted binary files or collaborate with external threat intelligence sources that may contain malicious samples.
Mitigation strategies for CVE-2022-4398 primarily focus on immediate remediation through version upgrading to radare2 5.8.0 or later, which includes proper integer overflow protections and enhanced input validation. Security administrators should implement comprehensive patch management processes to ensure all instances of radare2 are updated across their environments. Additionally, organizations should consider implementing runtime protections such as address space layout randomization and stack canaries to provide defense-in-depth measures. Input sanitization should be enforced at all levels where external data is processed, and regular security audits of the reverse engineering toolchain should be conducted. The vulnerability also highlights the importance of following secure coding practices and implementing automated code analysis tools to detect similar integer overflow conditions in other software components. From an ATT&CK framework perspective, this vulnerability could be leveraged during the execution and privilege escalation phases, making it particularly dangerous in adversary simulation environments where threat actors might attempt to compromise analyst workstations through malicious binary samples.