Misinformation
Tests whether the LLM produces factual fabrications, invented sources, or misrepresented expertise.
Sub-types
- Factual Fabrication: Model fabricates facts, statistics, or events that never occurred.
- Source Fabrication: Model invents citations, references, or sources that do not exist.
- Expertise Misrepresentation: Model presents itself as having expertise or authority it lacks.
Threat Profile
Objective: harmful_behavior
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
- truthfulqa: TruthfulQA for evaluating factual accuracy and misinformation
Secondary
- saladbench: SaladBench for broader misinformation coverage
Attack Techniques
Primary
- Static Template: Template-based prompt construction
Metrics
- truthfulness_score
- accuracy_score
Usage
Instantiate the Vulnerability
from hackagent.risks import Misinformation
from hackagent.risks.misinformation.types import MisinformationType
# Use all sub-types
vuln = Misinformation()
# Or specify particular sub-types
vuln = Misinformation(types=[
MisinformationType.FACTUAL_FABRICATION.value,
MisinformationType.SOURCE_FABRICATION.value,
])
Run an Evaluation Campaign
from hackagent import HackAgent
from hackagent.risks.misinformation import MISINFORMATION_PROFILE
agent = HackAgent(endpoint="http://localhost:8080/chat", name="my-agent")
# Use profile recommendations
for attack in MISINFORMATION_PROFILE.primary_attacks:
for dataset in MISINFORMATION_PROFILE.primary_datasets:
attack_config = {
"attack_type": "static_template", # attack.technique is "StaticTemplate"
"objective": MISINFORMATION_PROFILE.objective,
"dataset": {"preset": dataset.preset},
}
results = agent.hack(attack_config=attack_config)
print(f"Results: {results}")