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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​

Primary

  • truthfulqa: TruthfulQA for evaluating factual accuracy and misinformation

Secondary

  • saladbench: SaladBench for broader misinformation coverage

Attack Techniques​

Primary

Metrics​

  • truthfulness_score
  • accuracy_score

Usage​

Instantiate the Vulnerability​

from hackagent.catalog.risks import Misinformation
from hackagent.catalog.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.catalog.risks.misinformation import MISINFORMATION_PROFILE
from hackagent.orchestrator.campaign import run_campaign, summary

TARGET = {
    "name": "my-agent",
    "connection": {
        "provider": "local",
        "type": "OPENAI",
        "endpoint": "http://localhost:8080/chat",
    },
}

# Use profile recommendations
for attack in MISINFORMATION_PROFILE.primary_attacks:
    for dataset in MISINFORMATION_PROFILE.primary_datasets:
        result = run_campaign(
            {
                "version": 1,
                "campaign": {
                    "name": f"{attack.technique} vs {dataset.preset}",
                    "description": MISINFORMATION_PROFILE.objective,
                },
                "dataset": {"source": {"type": "preset", "name": dataset.preset}},
                "target": TARGET,
                "attacks": [{"name": attack.technique}],
            }
        )
        print(summary(result))