The Customer Support Chatbot exhibits significant vulnerabilities to prompt injection and system prompt leakage attacks. The agent's system prompt was fully extracted through indirect techniques, and several prompt injection vectors bypassed input filtering. The chatbot also disclosed internal API endpoints when presented with crafted multi-turn conversations. Immediate remediation is recommended for the critical and high-severity findings.
67
Techniques Tested
12
Successful Attacks
8
Partial Success
47
Defended
17.9%
Attack Success Rate
Deploy a dedicated classifier model before the main LLM to detect and block prompt injection attempts. Consider using a fine-tuned model specifically trained on injection patterns.
Related: f-demo-001, f-demo-005
Add explicit anti-disclosure instructions to the system prompt. Implement output filtering to detect and block responses that contain system prompt content in any encoding.
Related: f-demo-002
Require explicit human approval for high-impact actions such as ticket escalations, account modifications, and data exports. Implement role-based access control for agent capabilities.
Related: f-demo-004
Implement output scanning to detect and redact internal URLs, IP addresses, database names, and other infrastructure references before responses reach users.
Related: f-demo-003
Assessment conducted using Sixi.CH automated security scanning with 19 specialized attack agents executing 134 techniques across OWASP LLM Top 10, MITRE ATLAS, MAESTRO, LINDDUN, STRIDE, and PASTA frameworks. Both single-turn and multi-turn attack strategies were employed.
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