Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection
Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.
Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.
- Black-box visual injection reaches 80%+ ASR on open-weight and 47% on commercial frontier VLMs
- Attack triggers PII disclosure or precise, parseable malicious tool calls
- Optimized injections retain 43-46% ASR across commercial victims; cross-sample transfer retains 64-66%
- Demonstrated on OpenClaw: injected image overwrites TOOLS.md, enabling RCE and secret exfiltration
- Works where adaptive textual prompt injection fails; potential defenses discussed
- Vendors
- OpenAI
- Organizations
- OpenAI
- AI models
- Qwen3.6-27BGPT-5.5
From the original article
1 min read at arxiv.orgPrompt injection is widely recognized as a major security threat to AI agents that interact with untrusted external data, such as websites, documents, and emails. Prior work has shown that, in the text domain, black-box prompt injection can achieve near-perfect attack success rates (ASRs).
By Sizhe Chen · arxiv.org
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