CVE-2016-8714 in R Programming Language
Summary
by MITRE
An exploitable buffer overflow vulnerability exists in the LoadEncoding functionality of the R programming language version 3.3.0. A specially crafted R script can cause a buffer overflow resulting in a memory corruption. An attacker can send a malicious R script to trigger this vulnerability.
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Analysis
by VulDB Data Team • 09/17/2024
The vulnerability identified as CVE-2016-8714 represents a critical buffer overflow flaw within the R programming language's LoadEncoding functionality in version 3.3.0. This issue falls under the CWE-121 category of Stack-based Buffer Overflow, where insufficient bounds checking allows an attacker to overwrite adjacent memory locations. The vulnerability specifically affects how R processes encoded data during script loading operations, creating a pathway for arbitrary code execution through carefully crafted input sequences. The flaw exists in the memory management routines that handle data encoding and decoding processes, making it particularly dangerous in environments where R scripts are executed with elevated privileges or process untrusted input from external sources.
The technical exploitation of this vulnerability occurs when a malicious R script containing specially constructed encoding sequences is loaded into the R environment. The buffer overflow manifests during the LoadEncoding function execution where input data exceeds the allocated buffer size, causing memory corruption that can be leveraged to overwrite critical program variables or return addresses. This type of vulnerability aligns with ATT&CK technique T1059.001 for Command and Scripting Interpreter, where adversaries use scripting languages to execute malicious code. The flaw demonstrates poor input validation and memory handling practices, as the system fails to properly validate the length of encoded data before attempting to load it into fixed-size buffers. Attackers can exploit this by crafting R scripts that contain oversized encoding structures, triggering the buffer overflow condition that leads to unpredictable program behavior and potential remote code execution.
The operational impact of CVE-2016-8714 extends beyond simple memory corruption, potentially enabling complete system compromise when exploited in environments where R is used for data analysis, scientific computing, or statistical modeling. Organizations using R for processing external data sources, particularly in research institutions, financial services, or government agencies, face significant risk exposure. The vulnerability is particularly concerning in automated data processing pipelines where R scripts are executed without proper input sanitization, as it allows attackers to inject malicious code that could exfiltrate sensitive data, establish persistence mechanisms, or escalate privileges within the system. This represents a high-severity issue that requires immediate remediation in production environments, especially those handling sensitive or classified information. The vulnerability's exploitation capability makes it a prime target for advanced persistent threat actors seeking to establish long-term access to systems processing statistical data or scientific research.
Mitigation strategies for CVE-2016-8714 should prioritize immediate patching of R installations to versions that address the buffer overflow condition. System administrators must implement strict input validation procedures for all R script execution environments, particularly those processing external or untrusted data sources. Network segmentation and privilege separation should be enforced to limit the potential impact of successful exploitation attempts. Organizations should also consider implementing application whitelisting controls that restrict R script execution to trusted environments only. The vulnerability highlights the importance of regular security updates and vulnerability assessments in statistical computing environments, as these systems often process sensitive data without the same level of security scrutiny applied to traditional application environments. Security monitoring should include detection of unusual R script execution patterns and memory access violations that could indicate attempted exploitation of similar buffer overflow vulnerabilities.