High AUC from linear probes on model activations for indirect prompt injection does not license an unqualified claim of malicious-content detection, per a Qwen2.5-VL-7B case study with text and visual controls.
Agentvigil: Generic black-box red- teaming for indirect prompt injection against llm agents
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 9roles
background 1polarities
background 1representative citing papers
The paper builds SOPBench showing frequent SOP violations in agentic browsers and introduces SOPGuard to enforce the policy with low overhead in BrowserOS.
Agent-native LLMs are substantially more vulnerable to adversarial instructions arriving in tool descriptions than user messages (with the pattern reversing for general-purpose models and inverting again for tool outputs), as quantified by the new Safety Asymmetry Score across six models and three a
PI-Hunter automates red-teaming of LLM agents by generating and iteratively evolving source-aware test cases to induce retrieval of embedded malicious instructions from external environments.
Evidence-carrying multimodal agents decompose tool calls into predicates, obtain certificates from DOM/OCR/AX verifiers, and use a deterministic gate to authorize actions only when certificates support them, achieving zero unsafe executions in tested tasks.
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
Black-box optimization outperforms gradient-based methods for prompt injection on LLM agents, with success depending on attacker model strength and limited transfer from small to frontier models.
The paper argues that agent security is best addressed as a systems problem by applying principles from operating systems, networks, and formal methods rather than relying solely on model robustness improvements.
citing papers explorer
-
When AUC 0.998 Is Not Enough: A Candidate Evaluation Protocol for Hidden-State Probes of Indirect Prompt Injection in Multimodal Computer-Use Agents
High AUC from linear probes on model activations for indirect prompt injection does not license an unqualified claim of malicious-content detection, per a Qwen2.5-VL-7B case study with text and visual controls.
-
Same-Origin Policy for Agentic Browsers
The paper builds SOPBench showing frequent SOP violations in agentic browsers and introduces SOPGuard to enforce the policy with low overhead in BrowserOS.
-
Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models
Agent-native LLMs are substantially more vulnerable to adversarial instructions arriving in tool descriptions than user messages (with the pattern reversing for general-purpose models and inverting again for tool outputs), as quantified by the new Safety Asymmetry Score across six models and three a
-
PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
PI-Hunter automates red-teaming of LLM agents by generating and iteratively evolving source-aware test cases to induce retrieval of embedded malicious instructions from external environments.
-
Hallucination as Exploit: Evidence-Carrying Multimodal Agents
Evidence-carrying multimodal agents decompose tool calls into predicates, obtain certificates from DOM/OCR/AX verifiers, and use a deterministic gate to authorize actions only when certificates support them, achieving zero unsafe executions in tested tasks.
-
Progent: Securing AI Agents with Privilege Control
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
-
Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
-
Assessing Automated Prompt Injection Attacks in Agentic Environments
Black-box optimization outperforms gradient-based methods for prompt injection on LLM agents, with success depending on attacker model strength and limited transfer from small to frontier models.
-
Agent Security is a Systems Problem
The paper argues that agent security is best addressed as a systems problem by applying principles from operating systems, networks, and formal methods rather than relying solely on model robustness improvements.