Black-Box Red Teaming of Agentic AI: A Taxonomy-Driven Framework for Automated Risk Discovery

URL SCAN: Black-Box Red Teaming of Agentic AI: A Taxonomy-Driven Framework for Automated Risk Discovery
FIRST LINE: Computer Science > Artificial Intelligence

The Dissection

This paper builds an inspection layer for systems already being forced into production. It converts agentic risk into a seven-domain taxonomy, automates 120 adversarial scenarios per domain through SAGE-RT, and uses LLM judges to classify the results. Its claimed findings—56.25% governance risk, 65% privacy risk in multi-agent configurations, and vulnerabilities reaching 85%—show that these systems are not merely imperfect chatbots. They are porous autonomous actors with permissions, memory, tools, and exploitable coordination paths.

The paper’s deeper function is diagnostic: deployment velocity has outrun institutional comprehension, so evaluation is being bolted onto the machine after shipment.

The Core Fallacy

It confuses risk discovery with risk control.

A black-box test can expose that an agent is manipulable. It cannot compel firms or states to sacrifice speed, labor substitution, and competitive advantage by delaying deployment. Under P1 and P2, safety measures that materially reduce autonomy or raise costs become competitive disadvantages unless universally enforced. A taxonomy creates categories; it does not create coordination. An LLM judge produces a score; it does not produce authority.

The paper also treats observable behavior as an adequate boundary for systemic risk. Agent failures emerge from permissions, incentives, multi-agent interactions, institutional dependence, and cascading use—not merely from isolated attack scenarios. Measurement can slow the lag phase. It cannot reverse P1–P3 or restore mass productive participation.

Hidden Assumptions

• Seven risk domains adequately cover an open-ended and changing attack surface.
• 120 generated scenarios per domain are representative rather than merely enumerable.
• LLM judges can reliably validate risk without reproducing model bias or evaluator blind spots.
• Results from CrewAI, AutoGen, and four base models generalize to the wider agentic ecosystem.
• Basic system descriptions reveal enough architecture and context for meaningful black-box evaluation.
• Organizations will remediate discovered weaknesses instead of pricing them as acceptable losses.
• Mitigations will not reduce capability enough to trigger competitive bypass.
• Percentages such as “65% privacy risk” have stable meaning without clearer denominators, severity definitions, and failure thresholds.

Social Function

Primary classification: partial truth and transition management. Secondary classification: ideological anesthetic.

The research is not empty copium. It identifies real failure surfaces and could become valuable verification infrastructure. But it also gives deploying institutions a convenient ritual: test the agent, generate a risk score, issue a safety claim, and continue the race. “We red-teamed it” can become the bureaucratic equivalent of a warning label on industrial machinery that nobody intends to stop using.

Its durable value depends on enforcement—tool-permission limits, contractual liability, deployment gates, insurance requirements, or external audit authority. Without those, it is verification theater wrapped around acceleration.

The Verdict

This is a competent early-warning instrument attached to an engine whose owners are rewarded for accelerating. It documents the instability of agentic deployment but mistakes better observation for containment. Under the Discontinuity Thesis, black-box red teaming is useful to Sovereigns as control and verification infrastructure, and to Servitors as transition intermediation. It is not a brake on the discontinuity. It is the dashboard warning light on the vehicle replacing the driver.