AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations
AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations explores AttriGuard provides a novel defense against indirect prompt injection in LLM agents through causal attribution of tool invocations.. Commercial viability score: 7/10 in Security in LLMs.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $11K - $15K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisAgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context PurificationViability: 6.0
- Builds On ThisAdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMsViability: 3.0
- Builds On ThisBypassing AI Control Protocols via Agent-as-a-Proxy AttacksViability: 5.0
- Builds On ThisArbiter: Detecting Interference in LLM Agent System PromptsViability: 2.0
- Prior WorkDefense Against Indirect Prompt Injection via Tool Result Parsing