CVE-2019-11641 in Agave
Summary
by MITRE
Anomali Agave (formerly Drupot) through 1.0.0 fails to avoid fingerprinting by including predictable data and minimal variation in size within HTML templates, giving attackers the ability to detect and avoid this system.
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Analysis
by VulDB Data Team • 09/11/2023
The vulnerability identified as CVE-2019-11641 affects Anomali Agave version 1.0.0 and earlier, formerly known as Drupot, presenting a significant information disclosure and system fingerprinting risk. This weakness stems from the application's failure to implement proper randomization and variation in its HTML template outputs, creating predictable patterns that adversaries can exploit to identify and potentially evade detection by security systems. The vulnerability represents a critical flaw in the application's defensive design philosophy, as it inadvertently provides attackers with systematic means to recognize the system's presence and characteristics.
The technical root cause of this vulnerability lies in the application's template generation process, which consistently produces HTML content with predictable data patterns and minimal size variation. This predictable behavior creates a fingerprint that security researchers and attackers can analyze to determine if a target system is running Anomali Agave. The vulnerability aligns with CWE-200, which addresses information exposure, and CWE-201, which covers information exposure through predictable data generation. The predictable nature of the HTML templates allows for pattern recognition and systematic identification of the application across different environments, fundamentally undermining the system's ability to remain hidden or undetected.
From an operational perspective, this vulnerability significantly impacts the security posture of organizations using Anomali Agave, as it enables attackers to easily identify the presence of the application within their networks. The ability to fingerprint the system means that defensive measures can be tailored to specifically target or avoid detection by this particular software, potentially leading to more sophisticated attacks or evasion of security controls. This vulnerability also affects the application's intended security function, as it undermines the very purpose of providing threat detection and analysis capabilities when the system itself becomes easily recognizable by adversaries.
The impact extends beyond simple detection, as this fingerprinting capability can be leveraged in conjunction with other attack vectors, potentially enabling more targeted and effective attacks against systems running the vulnerable software. Attackers can use this information to craft more sophisticated social engineering campaigns or to develop specific exploits that target the identified system characteristics. The vulnerability also affects the application's ability to maintain operational security, as it creates a persistent signature that remains consistent across different deployments and environments, making it easier for threat actors to develop automated detection systems.
Organizations should implement immediate mitigations including randomizing HTML template outputs, introducing variable data patterns, and ensuring sufficient size variation in generated content to eliminate predictable signatures. The implementation of proper randomization techniques and content obfuscation methods would address the core issue by ensuring that each template generation produces unique enough content to prevent pattern recognition. Additionally, regular security assessments should verify that the mitigation measures remain effective and that no new predictable patterns are introduced through updates or modifications to the system. This vulnerability serves as a reminder of the critical importance of operational security considerations in application design and the need for thorough security testing that includes assessment of system fingerprinting characteristics.
The vulnerability demonstrates the importance of following security best practices outlined in frameworks such as the OWASP Top Ten and NIST cybersecurity guidelines, which emphasize the need for proper input validation and output randomization to prevent information disclosure. It also relates to ATT&CK technique T1566, which covers credential access through phishing and social engineering, as the ability to fingerprint the system can be used to create more convincing phishing campaigns targeting specific organizations. Organizations should also consider implementing network segmentation and monitoring to detect potential exploitation attempts, while ensuring that any security controls remain effective against systems that may be specifically designed to evade detection through predictable patterns.