CVE-2026-65056 in mcp-webresearch
Summary
by MITRE • 07/22/2026
mcp-webresearch 0.1.7 contains a server-side request forgery vulnerability that allows attackers to access internal network services by supplying loopback, link-local, or cloud metadata addresses to the visit_page tool, which only validates the URL protocol without filtering private or reserved IP ranges. Attackers can steer the LLM-controlled URL argument through prompt injection to navigate the server's Playwright browser to internal endpoints such as cloud instance metadata services, causing the server to return sensitive internal page content including credentials into the model context.
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Analysis
by VulDB Data Team • 07/22/2026
The mcp-webresearch 0.1.7 vulnerability represents a critical server-side request forgery flaw that fundamentally undermines network security boundaries through improper input validation. This vulnerability exists within the visit_page tool functionality where the system performs only protocol validation while completely neglecting the filtering of private or reserved IP address ranges including loopback addresses, link-local networks, and cloud metadata endpoints. The absence of comprehensive IP range validation creates an exploitable pathway where malicious actors can manipulate the application's behavior through carefully crafted URL inputs that bypass basic security checks.
The technical implementation of this vulnerability aligns with CWE-918, which specifically addresses server-side request forgery conditions where applications fail to properly validate and sanitize external resource requests. Attackers exploit this weakness by leveraging prompt injection techniques to manipulate the LLM-controlled URL argument, effectively steering the Playwright browser component to traverse internal network services that should remain isolated from external access. The vulnerability operates through a sophisticated chain of execution where the initial prompt manipulation results in the Playwright engine accessing internal endpoints such as cloud instance metadata services at addresses like 169.254.169.254 or similar reserved ranges.
The operational impact of this vulnerability extends far beyond simple information disclosure, creating potential pathways for privilege escalation and lateral movement within network environments. When the server's Playwright browser navigates to internal endpoints, it returns sensitive content including authentication credentials, configuration files, and system metadata directly into the model context where this information becomes available for further exploitation or exfiltration. This creates a dangerous feedback loop where the AI system potentially processes and retains sensitive data that was never intended for public exposure, fundamentally compromising the security posture of any environment where this tool is deployed.
The exploitation process typically involves constructing malicious URLs that contain private IP addresses or reserved network ranges, which the application accepts due to insufficient validation logic. The Playwright browser component executes these requests without proper network boundary enforcement, allowing access to internal services that normally would be protected by firewalls, network segmentation, or other security controls. This vulnerability represents a classic case of inadequate input sanitization and demonstrates how modern AI-assisted tools can inadvertently expose internal network infrastructure when proper security boundaries are not enforced at the application level.
Organizations should implement comprehensive network segmentation policies combined with strict URL validation mechanisms that explicitly block access to private IP ranges, loopback addresses, and cloud metadata endpoints. The recommended mitigations include deploying dedicated firewall rules, implementing proper input validation at multiple layers, and ensuring that all external resource access through automated tools is strictly controlled and monitored. Security controls should also incorporate regular penetration testing to identify similar vulnerabilities in AI-assisted applications, as these tools often present unique attack surfaces that traditional security measures might overlook. The vulnerability serves as a critical reminder of the importance of applying fundamental security principles even in emerging technology domains where new attack vectors continuously emerge.