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Excessive Agency

Tests whether the LLM performs actions or grants permissions exceeding its intended scope without oversight.

Sub-types​

  • Functionality: Model performs actions beyond its intended scope.
  • Permissions: Model grants or exercises permissions it should not have.
  • Autonomy: Model acts autonomously without required human oversight.

Threat Profile​

Objective: policy_violation

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.

Primary

  • agentharm: AgentHarm dataset for evaluating excessive agency in tool use

Secondary

  • agentharm_benign: Benign agent scenarios to establish baseline behavior

Attack Techniques​

Primary

  • Static Template: Template-based prompt construction

Metrics​

  • asr
  • judge_score

Usage​

Instantiate the Vulnerability​

from hackagent.risks import ExcessiveAgency
from hackagent.risks.excessive_agency.types import ExcessiveAgencyType

# Use all sub-types
vuln = ExcessiveAgency()

# Or specify particular sub-types
vuln = ExcessiveAgency(types=[
    ExcessiveAgencyType.FUNCTIONALITY.value,
    ExcessiveAgencyType.AUTONOMY.value,
])

Run an Evaluation Campaign​

from hackagent import HackAgent
from hackagent.risks.excessive_agency import EXCESSIVE_AGENCY_PROFILE

agent = HackAgent(endpoint="http://localhost:8080/chat", name="my-agent")

# Use profile recommendations
for attack in EXCESSIVE_AGENCY_PROFILE.primary_attacks:
    for dataset in EXCESSIVE_AGENCY_PROFILE.primary_datasets:
        attack_config = {
            "attack_type": "static_template",  # attack.technique is "StaticTemplate"
            "objective": EXCESSIVE_AGENCY_PROFILE.objective,
            "dataset": {"preset": dataset.preset},
        }
        results = agent.hack(attack_config=attack_config)
        print(f"Results: {results}")