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OSINT - How Malformed RTF Defeats Security Engines

Low
Unknowntlp:white
Published: Thu Mar 23 2017 (03/23/2017, 00:00:00 UTC)
Source: CIRCL
Vendor/Project: tlp
Product: white

Description

OSINT - How Malformed RTF Defeats Security Engines

AI-Powered Analysis

AILast updated: 07/02/2025, 17:11:44 UTC

Technical Analysis

The threat described involves the use of malformed Rich Text Format (RTF) files to evade detection by security engines. Malformed RTF files are crafted with intentional structural or syntactical anomalies that exploit weaknesses in the parsing logic of security tools such as antivirus scanners, intrusion detection systems, or sandbox environments. These malformed files can bypass signature-based or heuristic detection methods, allowing malicious payloads or exploits embedded within the RTF to execute without triggering alerts. Although the specific technical details are limited, the core issue is that security engines may fail to properly analyze or flag these malformed RTFs, creating an opportunity for attackers to deliver malware or conduct further exploitation. The threat level is indicated as low, and there are no known exploits in the wild, suggesting this is more of a proof-of-concept or a research finding highlighting a potential detection gap rather than an active widespread attack vector. The absence of affected versions or patches implies this is a generic technique rather than a vulnerability in a specific product. Overall, this threat highlights the importance of robust parsing and anomaly detection capabilities in security engines to handle non-standard or malformed document formats like RTF.

Potential Impact

For European organizations, the impact of this threat primarily lies in the potential for malware delivery or evasion of security controls via malicious RTF documents. Since RTF is a common document format used in email attachments and file sharing, attackers could exploit this technique to bypass perimeter defenses and deliver payloads such as ransomware, spyware, or credential stealers. The evasion of security engines could delay detection and response, increasing the risk of data breaches, operational disruption, or financial loss. However, given the low severity and lack of known exploits, the immediate risk is limited. Organizations with high exposure to document-based attacks, such as financial institutions, government agencies, and critical infrastructure operators, should be particularly mindful. The threat also underscores the need for layered defenses beyond signature-based detection, including behavioral analysis and sandboxing that can handle malformed inputs robustly.

Mitigation Recommendations

To mitigate this threat, European organizations should implement the following specific measures: 1) Ensure that security products such as antivirus, email gateways, and endpoint detection and response (EDR) solutions are regularly updated to improve parsing robustness and detection heuristics for malformed documents. 2) Employ advanced sandboxing technologies capable of safely executing and analyzing suspicious RTF files, including those with malformed structures, to detect malicious behavior beyond static signatures. 3) Implement strict email filtering policies that block or quarantine RTF attachments from untrusted or unknown sources, especially if unsolicited. 4) Train security teams to recognize the limitations of signature-based detection and to investigate anomalies or alerts related to document parsing failures. 5) Encourage users to avoid opening unexpected or suspicious RTF attachments and to report such incidents promptly. 6) Consider deploying document sanitization or conversion tools that normalize document formats before delivery to end users, removing potentially malicious constructs. These targeted actions go beyond generic advice by focusing on improving detection capabilities against malformed document evasion techniques and reducing the attack surface related to RTF files.

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Technical Details

Threat Level
3
Analysis
2
Original Timestamp
1490285621

Threat ID: 682acdbdbbaf20d303f0b9e0

Added to database: 5/19/2025, 6:20:45 AM

Last enriched: 7/2/2025, 5:11:44 PM

Last updated: 8/16/2025, 10:05:14 PM

Views: 13

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