CVE-2026-76993 in PentestGPT
Summary
by MITRE • 08/20/2026
A vulnerability was determined in GreyDGL PentestGPT up to 1.0.0. This vulnerability affects unknown code of the component Web-Page Crawling. Executing a manipulation of the argument Traceback can lead to injection. The attack can be executed remotely. A high complexity level is associated with this attack. It is stated that the exploitability is difficult. The exploit has been publicly disclosed and may be utilized. The reported GitHub issue was closed with the label "not planned".
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Analysis
by VulDB Data Team • 08/20/2026
The vulnerability identified in GreyDGL PentestGPT, specifically within versions up to 1.0.0, resides in the Web-Page Crawling component and involves a potential injection flaw triggered by manipulating the Traceback argument. This issue highlights a critical intersection between automated security testing tools and input validation mechanisms, where the tool itself becomes susceptible to exploitation due to insufficient sanitization of data derived from its own operational outputs or external inputs processed during web crawling activities. The core technical flaw lies in how the application handles the Traceback parameter, which is likely used for debugging or logging purposes but is subsequently passed into a context that allows for injection attacks without adequate encoding or validation checks. This lack of proper input sanitization creates an avenue for attackers to inject malicious payloads that can alter the intended execution flow or data processing logic within the crawling module.
From an operational perspective, this vulnerability poses significant risks because it affects a tool commonly used by security professionals and penetration testers who may handle sensitive target environments. The ability to execute remote attacks means that if PentestGPT is configured to crawl external web pages as part of its automated testing workflow, an attacker could potentially manipulate the crawling process itself. This could lead to unauthorized access to internal resources, data exfiltration through error-based injection techniques, or even command execution depending on how the traceback information is processed by underlying libraries or frameworks. The high complexity and difficult exploitability suggest that while the vulnerability exists, it requires specific conditions such as precise timing or particular state configurations in the application to be successfully leveraged, yet its public disclosure means that threat actors with sufficient expertise can develop functional exploits for this weakness.
In terms of industry standards classification, this issue aligns closely with CWE-79: Improper Neutralization of Input During Web Page Generation, commonly known as Cross-Site Scripting (XSS), or potentially CWE-89 if the injection targets SQL queries within a backend database interaction triggered by the crawling process. Additionally, from an ATT&CK framework perspective, this vulnerability relates to techniques involving input validation bypasses and could facilitate lateral movement or data collection phases in an attack chain if exploited against infrastructure running PentestGPT. The fact that the associated GitHub issue was closed with a "not planned" label indicates that the maintainers do not intend to address this specific vector through code changes, possibly due to perceived low risk, high complexity of remediation without breaking existing functionality, or architectural limitations in how the crawling component is designed.
Mitigation strategies for organizations utilizing PentestGPT must therefore focus on compensating controls rather than relying solely on vendor patches. Implementing strict input validation and output encoding at the application boundary before any data enters the web-crawling subsystem can significantly reduce the attack surface. Network-level protections such as Web Application Firewalls (WAFs) configured to detect injection patterns in traceback or similar debug parameters should be deployed to intercept malicious requests before they reach the vulnerable component. Furthermore, running PentestGPT within isolated environments with restricted network access and minimal privileges limits the potential impact of a successful exploitation attempt. Regular security assessments of custom configurations and third-party integrations are essential to identify such overlooked vulnerabilities in automated testing tools that may otherwise be assumed secure due to their role as defensive utilities.