arXiv:2606.30783v1 Announce Type: cross Abstract: We identify a security-fidelity tradeoff in defending LLMs against indirect prompt injection: defenses resist injected instructions largely by suppressing untrusted text, which corrupts tasks that must preserve it, such as translation and document editing. Attack-success metrics cannot see this, because a model that ignores an injection and one that faithfully processes it as data score identically. We introduce SecFid, a benchmark built so that executing an injection, processing it as data, and ignoring it produce distinguishable outputs. This
The rapid deployment and increasing sophistication of LLMs in critical applications necessitate a deeper understanding of their vulnerabilities and reliability, particularly against prompt injection.
This highlights a fundamental trade-off between LLM security and fidelity, revealing that current defense mechanisms inadvertently degrade desired model performance in certain contexts.
The focus shifts from merely preventing injections to developing nuanced defenses that can differentiate between malicious instructions and legitimate user data, demanding more sophisticated evaluation benchmarks.
- · AI security researchers
- · Developers of robust LLM evaluation platforms
- · Companies offering specialized LLM defense solutions
- · LLMs without sophisticated prompt injection defenses
- · Users relying on LLMs for sensitive, high-fidelity tasks
- · Attackers utilizing simple prompt injection techniques
Current prompt injection defenses are found to compromise the functionality of LLMs in tasks requiring faithful text preservation.
This drives the development of next-generation LLM architectures and defense strategies that can better distinguish between malicious commands and legitimate data inputs.
The increased complexity and cost of robust security measures might slow down the widespread adoption of LLMs in highly sensitive or regulated industries.
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Read at arXiv cs.AI