FIX: stop seeded converters from reseeding the global RNG

[Vishnu Rajeev (VishnuR23)]

Description

Three components seed Python's process-wide RNG when given a seed:

• ZalgoConverter.validate_input — random.seed(self._seed)
• ProportionSelectionStrategy.select_range (anchor="random") — random.seed(self._seed)
• WordProportionSelectionStrategy.select_words — random.seed(self._seed)

Each then draws from the random module itself. Because validate_input / select_* run on every conversion, a single seeded instance resets global random state repeatedly, and roughly 17 modules under pyrit/ draw from that same global RNG — CharSwapConverter, RandomCapitalLettersConverter, InsertPunctuationConverter, EmojiConverter, LeetspeakConverter, UnicodeConfusableConverter, SeedDataset sampling, and others.

The result: seeding one converter for reproducibility silently de-randomizes unrelated converters in the same process. For a red-teaming framework this quietly costs attack diversity — a campaign keeps re-testing the same variations while appearing randomized.

Reproduction on main — an unrelated converter, alongside a seeded ZalgoConverter:

caps = RandomCapitalLettersConverter(percentage=50.0)
zalgo = ZalgoConverter(seed=42)          # unseeded in the control run
for _ in range(4):
    await zalgo.convert_async(prompt="hello")
    print((await caps.convert_async(prompt="the quick brown fox jumps")).output_text)

control (ZalgoConverter())          -> 4 distinct outputs
main    (ZalgoConverter(seed=42))   -> 1 distinct output, repeated 4x
    thE QuIcK BROwn Fox JumPs
    thE QuIcK BROwn Fox JumPs
    thE QuIcK BROwn Fox JumPs
    thE QuIcK BROwn Fox JumPs

Fix

Each of the three now owns a random.Random instance and reseeds that rather than the global module. random.Random(seed) yields the same sequence as random.seed(seed) plus the module-level functions, so seeded output is byte-identical to before — I verified this by capturing outputs for seeds 1/42/123 on both sides of the change and diffing them. Only the global side effect is removed. grep -rn "random\.seed(" pyrit/ is now empty.

Note this does change one edge case: an unseeded instance no longer inherits a user's global random.seed(...). That path is what the per-component seed argument is for, and relying on it is what caused the bug.

Tests and Documentation

Three regression tests assert random.getstate() is unchanged across a seeded call — precise and non-flaky, no reliance on sampling luck:

• test_zalgo_seed_does_not_disturb_global_rng
• TestProportionSelectionStrategy::test_select_range_seed_does_not_disturb_global_rng
• TestWordProportionSelectionStrategy::test_select_words_seed_does_not_disturb_global_rng

All three fail on main and pass with the fix (confirmed by reverting only the source changes and re-running: 3 failed, 103 passed). Also added test_zalgo_seed_is_repeatable_on_same_instance and test_zalgo_unseeded_converters_stay_independent to pin both directions of the contract.

One existing test needed updating: test_char_swap_converter_proportion_unchanged_with_iterations patched random.sample to control word selection, which worked only because the strategy called the global module. It now patches the strategy's own RNG; the assertion it exists for (selection happens once, not per iteration) is unchanged.

Verification:

• pytest -n 4 --dist=loadfile tests/unit -> 15130 passed, 121 skipped
• pytest tests/unit/converter -> 1125 passed, 34 skipped
• pre-commit run --files <changed> -> all hooks pass, including ruff format, ruff check, and ty

No documentation changes — internal RNG ownership only, no public API or notebook surface affected.

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[Roman Lutz (romanlutz)]

This no longer guarantees the converter's documented reproducibility when Zalgo is composed with an unseeded random word selector. WordLevelConverter.convert_async() performs word selection after this reset, but WordProportionSelectionStrategy now owns an independent RNG, so repeated calls to ZalgoConverter(seed=42, word_selection_strategy=WordProportionSelectionStrategy(proportion=0.5)) can select different words and produce different outputs. Please either use one operation-local RNG across selection and mark generation, or explicitly define seeds as component-scoped and update the public contract accordingly. Add a regression test for this composed case.

[Roman Lutz (romanlutz)]

This regression test mutates the process-wide RNG and leaves it seeded at 0, which can make later tests order-dependent. The same pattern appears in both new selection-strategy tests. No setup seed is needed here: capture the existing random.getstate(), exercise the component, and compare against that state. Alternatively, restore the original state in a finally block.