PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in AgentDojo evaluations.
Melon: Provable defense against indirect prompt injection attacks in ai agents
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 6years
2026 6representative citing papers
PIIGuard uses optimized hidden HTML fragments on webpages to block LLMs from leaking contact PII via indirect prompt injection, achieving at least 97% defense success across tested models while preserving benign QA utility.
AgentVisor cuts prompt injection success rate to 0.65% in LLM agents with only 1.45% utility loss via semantic privilege separation and one-shot self-correction.
Seven cross-domain techniques for prompt injection detection are proposed; three implemented versions raise F1 scores on multiple benchmarks while releasing all code and data.
AgentShield uses layered deception traps in LLM agent tool interfaces to detect indirect prompt injection compromises with 90.7-100% success on commercial models, zero false positives, and cross-lingual transfer without retraining.
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.
citing papers explorer
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The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in AgentDojo evaluations.
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PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization
PIIGuard uses optimized hidden HTML fragments on webpages to block LLMs from leaking contact PII via indirect prompt injection, achieving at least 97% defense success across tested models while preserving benign QA utility.
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AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
AgentVisor cuts prompt injection success rate to 0.65% in LLM agents with only 1.45% utility loss via semantic privilege separation and one-shot self-correction.
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Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection
Seven cross-domain techniques for prompt injection detection are proposed; three implemented versions raise F1 scores on multiple benchmarks while releasing all code and data.
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AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents
AgentShield uses layered deception traps in LLM agent tool interfaces to detect indirect prompt injection compromises with 90.7-100% success on commercial models, zero false positives, and cross-lingual transfer without retraining.
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ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.