AgentXploit Exploiter achieves 79.2% indirect prompt injection success against GPT-4.1 agents on AgentDojo via iterative runtime feedback

Auto-published from arXiv:2609.31318 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.92, flags: [possible-zero-day, no-prompt-excerpt])

Category

indirect-prompt-injection

Model

GPT-4.1 (victim agents); gpt-5.1-codex (AgentXploit attacker backbone)

Surface

agent harness (AgentDojo task framework)

Setup

The AgentXploit Exploiter Agent is given known injection points in the AgentDojo environment and iteratively (up to 50 refinement rounds) crafts and revises indirect prompt injection payloads targeting GPT-4.1 victim agents. Two key mechanisms are used: (1) execution-trace feedback — the Exploiter observes runtime traces and adjusts payloads accordingly; (2) multi-injection coordination — simultaneous payloads across multiple injection sites. The paper explicitly excludes verbatim payload text, but the full system and benchmark are released publicly.

Reproduction anchor

code: https://github.com/lwd17/AgentXploit; benchmark: AgentDojo (standard evaluation suite, injection-point-provided mode)

Observed behavior

GPT-4.1 victim agents are successfully exploited in 79.2% of AgentDojo tasks (vs. prior SOTA AgentVigil at 52.7%). Execution-trace feedback alone drives success from 46.2% to 69.1% across refinement rounds; multi-injection coordination reaches 74.9%.

Expected behavior

Victim agents should detect and refuse adversarially injected instructions embedded in tool outputs or environmental data, maintaining their original task goals regardless of what untrusted content contains.

Reproducibility

high

Threat model

Deployed AI agents that process untrusted environmental content (email bodies, web pages, database records) are vulnerable to indirect prompt injection at scale; an autonomous red-team tool like AgentXploit can discover, craft, and refine exploits against GPT-4.1-class production agents without human expertise, dramatically lowering the bar for large-scale agent compromise.

Novelty

First fully autonomous two-stage system (Analyzer + Exploiter with runtime feedback) to achieve SOTA attack success on AgentDojo, surpassing handcrafted and prior LLM-assisted approaches by 26+ percentage points and demonstrating that iterative execution-trace feedback is the dominant factor driving high attack success rates.

Source

• arXiv: 2609.31318
• PDF: https://arxiv.org/pdf/2609.31318
• Categories: cs.CR, cs.AI
• Authors: Weida Liang, Shi Qiu, Zhun Wang, Simon Sure, Xiaoyuan Liu, Tianneng Shi, Zhaorun Chen, Wenbo Guo, Dawn Song

Triage notes (auto)

• paperType: red-team-vuln
• estimatedCaseCount: 3
• triage reason: Presents AgentXploit-Bench: 72 reproducible vulnerabilities across 12 open-source AI-agent systems (path traversal, injection, tool misuse) with 59.3% end-to-end exploitation success. Recent submission with unreferenced vulnerabilities and no explicit vendor-disclosure statement.

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