AIRGuard: Guarding Agent Actions with Runtime Authority Control

AIRGuard is a runtime security framework that protects AI agents from authority confusion attacks, where attackers manipulate untrusted context to misuse authorized tool access. The system reduces attack success rates from 36.3% to 5.5% while maintaining 76% of benign functionality, outperforming existing defense mechanisms by enforcing least-privilege authorization at execution time.

The emergence of tool-using language agents introduces a novel attack surface distinct from traditional AI safety concerns. While jailbreaks target model outputs, agent attacks exploit the authority gap between reasoning and execution—attackers inject untrusted context that steers legitimate tool access toward malicious outcomes. AIRGuard addresses this vulnerability by implementing runtime authority control, treating tool execution as a distinct authorization layer separate from model reasoning.

This work reflects growing recognition that AI safety requires architectural solutions beyond prompt engineering. As agents gain access to file systems, APIs, and external services, the potential for systemic harm scales dramatically. Previous defenses like ARGUS and MELON achieved only 52% and 42% utility preservation respectively, indicating that naive defensive approaches severely degrade agent functionality. AIRGuard's comparative success—76% utility at substantially lower attack rates—demonstrates that sophisticated runtime monitoring can achieve security without crippling usability.

The framework's approach normalizes heterogeneous tool calls, derives granular step-level authority from task-level permissions, and simulates sensitive operations before execution. This architecture matters for enterprises deploying autonomous agents in production environments where both security breaches and false positives carry costs. The research validates that a dedicated runtime layer substantially outperforms prompt-only policies, suggesting future agent platforms will require similar guardrails.

Looking forward, AIRGuard's open-source release invites community hardening and extension. The framework becomes particularly relevant as AI agents gain access to cryptocurrency operations, trading systems, and financial APIs where authority confusion could cause direct economic damage. Developers building agent infrastructure should monitor whether runtime authority control becomes an industry standard.

• →AIRGuard reduces agent attack success from 36.3% to 5.5% by enforcing least-privilege authorization at execution time rather than relying on model reasoning alone
• →Runtime authority control outperforms prompt-only defenses, with 76% benign utility preservation compared to 52% for existing approaches
• →Authority confusion represents a distinct attack class where untrusted context steers legitimate tool access toward malicious outcomes
• →The framework normalizes heterogeneous tool calls and audits cross-step risk before allowing sensitive side effects to execute
• →Open-source availability enables broader adoption of runtime security layers in agent-based systems handling critical operations