CVE-2016-1000222 in Logstash
Summary
by MITRE
Logstash prior to version 2.1.2, the CSV output can be attacked via engineered input that will create malicious formulas in the CSV data.
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Analysis
by VulDB Data Team • 10/18/2019
The vulnerability identified as CVE-2016-1000222 represents a critical security flaw in Logstash versions prior to 2.1.2 that affects the CSV output functionality. This vulnerability stems from insufficient input validation and sanitization within the CSV plugin, creating a potential vector for formula injection attacks that can have far-reaching consequences in data processing environments. The issue specifically impacts systems where Logstash is used to process and export data to CSV format, particularly in scenarios involving untrusted data sources or user-provided inputs that are subsequently formatted into CSV output files.
The technical flaw manifests when Logstash processes data containing specially crafted input that includes CSV formula prefixes such as equals signs, plus signs, or other characters that trigger formula execution in spreadsheet applications. When these malicious inputs pass through the CSV output plugin without proper sanitization, they create CSV data that, when opened in applications like Microsoft Excel, Google Sheets, or other spreadsheet software, automatically executes malicious commands or retrieves external resources. This type of vulnerability falls under the CWE-119 weakness category, specifically related to improper input validation and inadequate sanitization of user-supplied data within data processing pipelines. The vulnerability enables attackers to potentially execute arbitrary code, perform data exfiltration, or manipulate spreadsheet behavior through carefully constructed input data.
The operational impact of this vulnerability extends beyond simple data corruption or display issues, as it creates a potential attack surface that can be exploited in supply chain attacks or insider threat scenarios. Organizations using Logstash for data processing, particularly those that export data to CSV formats for further analysis or sharing, face significant risk when operating vulnerable versions. The vulnerability can be exploited through various attack vectors including compromised data sources, malicious user inputs, or through manipulation of data streams that flow through Logstash processing pipelines. This attack vector aligns with the ATT&CK technique T1059.006 for Command and Scripting Interpreter, specifically targeting spreadsheet applications that interpret CSV formulas as executable commands. The risk is amplified in environments where CSV files are frequently shared or opened in spreadsheet applications, as the malicious formulas can execute automatically without user interaction, potentially leading to full system compromise or data loss.
Mitigation strategies for CVE-2016-1000222 require immediate patching of Logstash installations to version 2.1.2 or later, which includes proper input sanitization and validation within the CSV output plugin. Organizations should implement additional defensive measures such as input validation at multiple layers within their data processing pipelines, including sanitizing data before it reaches the CSV output stage. Network segmentation and access controls should be enforced to limit exposure of vulnerable Logstash instances, while monitoring should be implemented to detect unusual data processing patterns or attempts to inject malicious input. Security teams should also consider implementing automated vulnerability scanning and penetration testing to identify other potential injection points in their data processing workflows, particularly focusing on data export functions and third-party integrations. The remediation process must include comprehensive testing of patched environments to ensure that legitimate data processing continues to function correctly while eliminating the vulnerability. Regular security updates and vulnerability management processes should be strengthened to prevent similar issues in other components of the data processing stack, as this vulnerability demonstrates the critical importance of proper input sanitization in data handling applications.