Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping
arXiv:2606.18120v1 Announce Type: cross Abstract: Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace {{x}} expression HTML-escapes the interpolated value and is documented as the safe default; its triple-brace {{{x}}} expression inserts the value raw. We show that this choice silently governs an application's exposure to structural role injection, where attacker-controlled data carries chat role delimiters that forge a higher-privilege turn. A mod
The proliferation of LLM-powered applications using templating engines like Handlebars necessitates immediate attention to security vulnerabilities in prompt construction, particularly those arising from interpolation methods.
This highlights a critical, under-addressed security vulnerability (structural role injection) in LLM application development that could lead to privilege escalation and data breaches, affecting widespread enterprise and consumer systems.
Software development practices for LLM applications must incorporate stricter security-by-design principles, emphasizing secure templating and validation of user-controlled inputs to prevent novel prompt injection attacks.
- · Cybersecurity firms
- · Secure AI development platforms
- · Developers expert in prompt engineering security
- · Applications using vulnerable templating practices
- · Organizations with inadequate AI security protocols
- · Open-source LLM frameworks lacking guardrails
Immediate patches and security advisories will be issued for affected templating engines and LLM frameworks.
Increased adoption of formal verification methods and secure coding standards specifically for LLM-integrated software.
The emergence of new security tooling and auditing services specialized in detecting and mitigating structural role injection and similar LLM-specific vulnerabilities.
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