CVE-2026-67598

CVE-2026-67598 in Emlog Pro reveals a vulnerability that extends far beyond a simple TLS bypass. The AI integration code unconditionally disables SSL verification across all four outbound request methods—sendStream, sendImageRequest, and two others—with no mechanism to re-enable it. A MITM attacker who intercepts traffic doesn't passively harvest credentials; they inject malicious AI responses that the query_database and update_config handlers execute as autonomous actions. This collapses what would normally be a multi-stage compromise (credential theft followed by lateral movement) into a single intercept. The critical insight is that the TLS failure is merely the entry vector. The actual amplification lives in the tool-call pipeline: query_database exposes your full schema to AI-driven queries, enabling both exfiltration and relationship mapping an attacker couldn't otherwise perform. update_config is worse—it modifies system state based on AI-injected instructions with no sanitization layer between response and execution. This is command injection wearing AI clothing. The AI becomes a programmable proxy whose instructions flow through the same compromised channel. The architectural assumption baked into this code is the real problem: the developer treated the AI provider as a trusted control plane rather than an external service requiring output validation. This mirrors the same 'internal service' mental model that produced two decades of SSRF and API security failures. The pattern—trusting AI outputs as executable commands over an unverifiable channel—almost certainly exists in other AI-integrated applications. For defenders: audit any code path where AI responses flow directly into database queries or configuration updates without an explicit validation boundary. Treat AI output as untrusted input regardless of transport security, because the transport layer is never the only risk to an AI integration pipeline. The fix requires both re-enabling TLS verification and adding output schema enforcement, permission boundaries, or dry-run capabilities between AI response and tool execution.

Reviewed through automated stages and approved by a human before publication.