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Other Encodings

The other encodings strategies provide additional text transformation methods to test model resilience against evasion techniques that use alternative text representations.

Morse Code​

The Morse code strategy converts all characters in the test payload to dots and dashes, the universal encoding system developed for telegraph communications.

How It Works​

Standard ASCII characters are converted to their Morse code equivalents:

  • Letters are converted to sequences of dots (.) and dashes (-)
  • Spaces between words are replaced with forward slashes (/)
  • Characters without a Morse code equivalent remain unchanged

For example, "Hello World" becomes:

.... . .-.. .-.. --- / .-- --- .-. .-.. -..

Configuration​

Add the Morse code strategy to your red team configuration:

promptfooconfig.yaml
strategies:
  - morse # Apply Morse code transformation

Pig Latin​

The Pig Latin strategy transforms text according to the playful language game rules of Pig Latin, which is a simple form of language encoding.

How It Works​

The transformation follows these rules:

  • For words beginning with consonants, the initial consonant cluster is moved to the end and "ay" is added
  • For words beginning with vowels, "way" is added to the end
  • Punctuation and numbers remain unchanged

For example, "Hello World" becomes:

elloHay orldWay

Configuration​

Add the Pig Latin strategy to your red team configuration:

promptfooconfig.yaml
strategies:
  - piglatin # Apply Pig Latin transformation

Example​

Here's a complete example that applies these encoding transformations to test cases:

promptfooconfig.yaml
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
prompts:
  - 'Answer this question: {{prompt}}'

providers:
  - openai:gpt-4.1

# Red team config
redteam:
  plugins:
    - owasp:llm
  strategies:
    - basic # Include original prompts
    - morse # Apply Morse code transformation
    - piglatin # Apply Pig Latin transformation

Technical Details​

These encoding transformations are static, deterministic processes that don't require additional API calls to implement. They provide a way to test how models handle content that has been transformed in ways that might bypass text-based content filters while remaining human-interpretable.

Security Considerations​

Alternative text encodings can be used to bypass content filters that rely on exact string matching. By replacing standard text with these encoded versions, attackers might be able to evade detection of prohibited content. These strategies help evaluate how well models handle these types of evasion techniques.

Some specific security benefits of testing with these encodings:

  • Identifies weaknesses in content moderation systems that rely on keyword matching
  • Tests model comprehension of obfuscated harmful content
  • Evaluates guardrail effectiveness against simple transformation techniques
  • Helps develop more robust safety mechanisms for public-facing AI applications

See Also​