Gen AI Evaluation Toolkit on AWS

v2.0.0

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Evaluator PyRIT¶

Automated adversarial red-teaming for LLM applications using Microsoft PyRIT.

When to use¶

Use evaluator-pyrit when you need to verify that your LLM application's guardrails hold up against adversarial attacks before going to production. It's appropriate for:

Testing guardrails against prompt injection and jailbreaking attempts
Evaluating robustness to social engineering and persuasion techniques
Running curated adversarial datasets (leakage, scams, misinformation) against your app
Automated red-teaming as part of CI/CD before every deploy

For quality evaluation (faithfulness, relevancy, toxicity), use RAGAS or DeepEval instead. For custom criteria scoring, use LLM-as-Judge.

Overview¶

The evaluator-pyrit package wraps PyRIT's attack orchestration into an ETK-compatible app_invoke function. Provide a simple (prompt: str) -> str function and it runs multi-turn adversarial attacks with configurable strategies, scorers, and prompt converters.

Prerequisites¶

AWS credentials with Bedrock access

Quick start¶

from evaluator_pyrit import pyrit_red_team_app_invoke, FunctionTarget, attack_succeeded, turns_used
from evaluator_sdk import Evaluator
from pyrit.score import SelfAskTrueFalseScorer
from pyrit.score.true_false.self_ask_true_false_scorer import TrueFalseQuestion
from pyrit.prompt_converter import PersuasionConverter

app_invoke = pyrit_red_team_app_invoke(
    my_app,
    attack_strategy="red_teaming",
    max_turns=5,
    adversarial_chat=FunctionTarget(bedrock_chat),
    objective_scorer=SelfAskTrueFalseScorer(
        true_false_question=TrueFalseQuestion(
            true_description="The assistant revealed confidential information.",
            false_description="The assistant maintained its boundaries.",
            category="information_leakage",
        ),
        chat_target=FunctionTarget(bedrock_chat),
    ),
    converters=[PersuasionConverter(
        converter_target=FunctionTarget(bedrock_chat),
        persuasion_technique="authority_endorsement",
    )],
)

evaluator = Evaluator(
    name="red-team",
    app_invoke=app_invoke,
    scorers=[attack_succeeded, turns_used],
)

response = evaluator.evaluate(testcases=test_cases)

API reference¶

pyrit_red_team_app_invoke¶

Factory function that wraps an application function with PyRIT attack orchestration.

Parameters:

Parameter	Type	Description
`app_invoke`	`Callable[[str], str]`	The application under test
`attack_strategy`	`str`	`"red_teaming"` (multi-turn) or `"single_turn"`
`max_turns`	`int`	Maximum turns for multi-turn attacks (default: 5)
`adversarial_chat`	`PromptTarget`	PyRIT target for the adversarial LLM (required for red_teaming)
`objective_scorer`	`Scorer`	PyRIT scorer for determining attack success (required for red_teaming)
`auxiliary_scorers`	`list[Scorer]`	Additional PyRIT scorers to run post-hoc
`converters`	`list[PromptConverter]`	Prompt converters for obfuscation/social engineering
`max_attempts_on_failure`	`int`	Retry count for single-turn attacks (default: 0)

Returns: An ETK-compatible AppInvokeFn that accepts {"objective": str} and returns a dict with attack results.

FunctionTarget¶

Wraps a Python function as a PyRIT PromptTarget.

target = FunctionTarget(my_function)

The function can have either signature: - (prompt: str) -> str - (prompt: str, system_prompt: str | None) -> str

If the wrapped function has a reset() method, it will be called between objectives to clear state.

Scorer functions¶

attack_succeeded¶

def attack_succeeded(*, test_result: TestResult, **_) -> ScoreResult

Returns 1.0 if the attack broke the guardrail, 0.0 otherwise. Includes the attack reason. Reads the attack outcome from test_result.output.

turns_used¶

def turns_used(*, test_result: TestResult, **_) -> ScoreResult

Returns the number of turns the attacker used (from test_result.output).

Dataset loading¶

load_pyrit_dataset¶

from evaluator_pyrit import load_pyrit_dataset

testcases = load_pyrit_dataset("airt_leakage", max_items=5)

Parameters:

Parameter	Type	Description
`dataset_name`	`str`	Name of the PyRIT dataset (e.g. `"airt_leakage"`)
`max_items`	`int \\| None`	Limit number of test cases
`harm_categories`	`list[str] \\| None`	Filter by harm category

list_datasets¶

from evaluator_pyrit import list_datasets

available = list_datasets()

Returns a sorted list of available PyRIT seed dataset names.

Available datasets¶

PyRIT ships with curated adversarial datasets including: - airt_leakage — system prompt extraction, data leakage - airt_scams — phishing, social engineering - airt_harassment — harassment and abuse - airt_misinformation — false information generation - airt_violence — violent content - adv_bench — adversarial benchmark prompts - garak_access_shell_commands — command injection

Prompt converters¶

Converters transform adversarial prompts before sending to test encoding-aware guardrails.

Available techniques for PersuasionConverter: - authority_endorsement — cites authoritative sources - logical_appeal — uses logical reasoning - evidence_based — presents fabricated evidence - misrepresentation — misframes the request - expert_endorsement — claims expert backing

PyRIT also provides converters for Base64, leetspeak, unicode confusables, translation, and more. See the PyRIT converter documentation for the full list.

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