Perturbation

Module: dreadnode.transforms.perturbation

Character-level and token-level noise that tests robustness of text classifiers and safety filters.

Transform	Description
random_capitalization	Randomize letter casing
insert_punctuation	Insert random punctuation
diacritic	Add diacritical marks to characters
underline	Add Unicode underline combining marks
character_space	Insert spaces between characters
zero_width	Insert zero-width characters
zalgo	Apply Zalgo text (stacked combining marks)
unicode_confusable	Replace with Unicode confusables
unicode_substitution	Substitute with visually similar Unicode
repeat_token	Repeat tokens to confuse tokenizers
emoji_substitution	Replace words with emoji equivalents
token_smuggling	Split tokens across boundaries
semantic_preserving_perturbation	Meaning-preserving noise
instruction_hierarchy_confusion	Confuse instruction priority parsing
context_overflow	Overflow context window
gradient_based_perturbation	Gradient-inspired token perturbation
multilingual_mixing	Mix multiple languages
cognitive_hacking	Exploit cognitive biases in processing
payload_splitting	Split payload across inputs
attention_diversion	Divert model attention
style_injection	Inject style directives
implicit_continuation	Exploit continuation behavior
authority_exploitation	Exploit authority patterns
linguistic_camouflage	Linguistically camouflage intent
temporal_misdirection	Use temporal framing to misdirect
complexity_amplification	Amplify prompt complexity
error_injection	Inject deliberate errors
encoding_nesting	Nest multiple encodings
token_boundary_manipulation	Manipulate tokenizer boundaries
meta_instruction_injection	Inject meta-level instructions
sentiment_inversion	Invert sentiment cues
simulate_typos	Add realistic typographical errors

See Transforms for how to apply transforms with any attack.