# Hypothetical Response / Function Masking Token Smuggling

- **Severity:** High
- **Signature ID:** `c27ace72-ccdb-4c47-88c6-aac25075611b`
- **MITRE ATLAS:** AML.T0051 (LLM Prompt Injection)
- **OWASP:** ASI01 (Agent Authorization and Control Hijacking), LLM01 (Prompt Injection)

## Summary

Attackers can get an AI model to produce harmful content by disguising the request as fiction or as a word-puzzle instead of asking directly. This rule flags two known patterns for doing that: 'hypothetical response' framing and 'function masking' puzzles.

## How the attack works

The attacker writes a prompt that never directly asks for the harmful content. In the first variant, they ask the model to write a 'hypothetical response' from the perspective of a fictional character who is planning to do something harmful, then to rewrite that response in detail as if it were real instructions. In the second variant, they define a fake function like predict_mask('to <mask> property unlawfully') = rob, and then ask a related question ('how does someone rob a store?'), leaning on the established word-substitution to get the model to answer directly. Both approaches add a layer of abstraction — fiction or puzzle logic — so the model's safety filters see a benign-looking request rather than the harmful one underneath.

## Why it matters

If successful, the model outputs the harmful instructions or content the attacker wanted (e.g. how to commit a crime), even though a direct request would have been refused. This is a jailbreak of the model's own safety behavior, not a system compromise, but it can be chained into further misuse of an AI agent's capabilities.

## What you can do

- Review prompts that combine fictional/hypothetical framing with requests for step-by-step detail or 'in-character' rewrites of instructions.
- Watch for prompts that define custom functions or word-substitution mappings (like predict_mask) immediately followed by a related real-world question.
- Apply output-side content filtering in addition to input-side prompt filtering, since these techniques are designed to slip past input-only checks.
- Distinguish legitimate creative-writing or NLP-research requests from ones that end with a direct, actionable, real-world harmful question — the pivot from fiction/puzzle back to a literal request is the key signal.

## Known benign look-alikes

- Legitimate creative writing requesting fictional character perspectives without harmful instruction
- Language model API documentation showing predict_mask() examples with benign words
- NLP research papers showing masked language model examples

## References

- https://agentthreatrule.org/en/rules/ATR-2026-00272
- https://github.com/NVIDIA/garak/blob/main/garak/probes/smuggling.py
- https://guzey.com/ai/two-sentence-universal-jailbreak/
- https://medium.com/@austin-stubbs/llm-security-types-of-prompt-injection-d7ad8d7d75a3

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Source: https://www.netzilo.com/threats/atr-hypothetical-response-function-masking-token-smuggling