Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs
Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. This creates a steganographic exfiltration risk that is difficult to detect with output-level steganalysis. Recent work proposes mechanistic detection using linear probes that recover the secret from internal activations. We show that this defense can be systematically evaded, but that detectability can be recovered through a targeted data-level intervention. First, we extend the detection setup to include a non-linear MLP probe. We then adversarially fine-tune steganographic trojans across five base models: Qwen3-8B, Llama-3.1-8B, Ministral-8B, Qwen3-14B, and Phi-4-14B. The resul...
| Authors | Charles Westphal, Timothy Douglas, Keivan Navaie et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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