task_categories:

• text-generation
  language:
• en
  pretty_name: BAD ACTS
  size_categories:
• n<1K

BAD-ACTS Dataset

Dataset Card for BAD-ACTS: Benchmark of ADversarial ACTionS

BAD-ACTS is a dataset of adversarially induced harmful actions designed to benchmark the robustness of agentic systems. It is introduced in the paper:

Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harmful Actions (2025)

This dataset accompanies the BAD-ACTS benchmark and contains examples of adversarial actions crafted to elicit harmful behavior in agentic systems across a range of task environments.

Dataset Structure

Each entry in the dataset includes:

• Environment: The task setting (e.g., travel_planning, code_generation)
• Category: High-level classification of the harmful behavior
• Sub-Category: Fine-grained classification of the harmful behavior
• Harmful_Behavior: The intended harmful action targeted by the adversary
• Target: The agent in the environment targeted by the attack
• Keyword: A critical keyword used for evaluation

Supported Environments

• Travel Planning
• Financial Article Writing
• Code Generation
• Multi-Agent Debate

How to Use

You can load the dataset directly using the 🤗 Datasets library:

from datasets import load_dataset

dataset = load_dataset("JNoether/bad-acts")

Dataset Size

• Total examples: 188
• Annotations: Human-reviewed and edited to ensure high quality and diversity

Intended Use

This dataset is intended for:

• Evaluating the adversarial robustness of LLM agents
• Studying failure modes and harmful behaviors
• Training or fine-tuning safety-enhancing models
• Developing defense mechanisms (e.g., guardian agents, prompt shields)