CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering
Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that suppresses this behavior inside the model. Per model, a five-step recipe fits a residual-stream direction from paired episodes differing only in whether an embedded instruction is followed, and retains it only if it passes pre-specified causal and capability gates. At deployment, the direction is subtracted from every tool-result token during prefill. The edit is always on--there is no detection decision to evade--and requires no fine-tuning, auxiliary model, or added tokens, only white-box serving and tool-result span boundaries. Across five...
| Authors | Mark Russinovich |
|---|---|
| Primary category | cs.CR |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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