Craft Adversarial Data
Tests whether adversarially crafted data — perturbations, poisoned examples, or augmentation abuse — can compromise model behaviour.
Sub-types
- Perturbation Attacks: Small, imperceptible changes to inputs that alter model outputs.
- Poisoned Examples: Adversarially crafted examples designed to trigger specific model failures.
- Data Augmentation Abuse: Exploiting data augmentation pipelines to inject adversarial samples.
Threat Profile
Objective: jailbreak
Objective vs. goals
The objective above selects the scoring rubric for a run — it is one of a
fixed set of built-in names, not something you write. The goals you pass to an
attack are separate free-text strings that you author yourself. See
Goals vs. objective.
Recommended Datasets
Primary
- advbench: Adversarial goals that may involve crafted perturbations
Attack Techniques
Primary
- Static Template: Template-based prompt construction
Metrics
- asr
- judge_score
Usage
Instantiate the Vulnerability
from hackagent.risks import CraftAdversarialData
from hackagent.risks.craft_adversarial_data.types import CraftAdversarialDataType
# Use all sub-types
vuln = CraftAdversarialData()
# Or specify particular sub-types
vuln = CraftAdversarialData(types=[
CraftAdversarialDataType.PERTURBATION_ATTACKS.value,
CraftAdversarialDataType.POISONED_EXAMPLES.value,
])
Run an Evaluation Campaign
from hackagent import HackAgent
from hackagent.risks.craft_adversarial_data import CRAFT_ADVERSARIAL_DATA_PROFILE
agent = HackAgent(endpoint="http://localhost:8080/chat", name="my-agent")
# Use profile recommendations
for attack in CRAFT_ADVERSARIAL_DATA_PROFILE.primary_attacks:
for dataset in CRAFT_ADVERSARIAL_DATA_PROFILE.primary_datasets:
attack_config = {
"attack_type": "static_template", # attack.technique is "StaticTemplate"
"objective": CRAFT_ADVERSARIAL_DATA_PROFILE.objective,
"dataset": {"preset": dataset.preset},
}
results = agent.hack(attack_config=attack_config)
print(f"Results: {results}")