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HBT-INC-0165 · Dimension INC · Incentives

Jailbreak

Bypassing model safety constraints.

Risk·AI-Behavioral Coupling·Grade C·draft· enriching…
In one paragraph

Jailbreak is bypassing model safety constraints. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0165, within the Risk family. The core principle: bypassing model safety constraints. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.

Scientific Definition

Bypassing model safety constraints.

Plain-English Definition

Bypassing model safety constraints.

Feynman Explanation

Every safety system has a creative attack surface.

Core Principle

Bypassing model safety constraints.

Mechanisms

Psychological

Pending editorial review.

Behavioral Economic

Pending editorial review.

Neurological

Pending editorial review.

Evolutionary

Pending editorial review.

Sociological

Pending editorial review.

Computational

Pending editorial review.

Systems

Pending editorial review.

Inputs (Triggers)

Pending editorial review.

Outputs (Behaviors)

Pending editorial review.

Behavioral Signature

Every safety system has a creative attack surface.

Examples

Everyday
  • Role-playing exploits that bypass content policies.
Modern (Organizational)
  • Risk in user-facing AI products.
Historical

Pending editorial review.

Lab Commentary

Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.

Why this element matters to incentive design

Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: every safety system has a creative attack surface. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.

How it gets exploited

Left undesigned, risk in user-facing AI products. It is amplified whenever risk in user-facing AI products. Inside organizations that shows up as risk in user-facing AI products. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.

How the Lab designs around it

The redesign move is to defense in depth. Continuous red-teaming. Don't rely on model alignment alone. Measure the behavior, not the sentiment. A survey will tell you how people feel about this; only observed action tells you whether it changed.

Famous Experiments

Pending editorial review.

Design Principles

  • Defense in depth. Continuous red-teaming. Don't rely on model alignment alone.

Measurement Approaches

Pending editorial review.

Evidence

Evidence Grade
C (A strongest → E speculative)
Replication
★★☆☆☆
Intervention Confidence
3 / 5
Consensus
Pending editorial review (HBT v1.0 auto-seed).
Limitations
Pending editorial review (HBT v1.0 auto-seed).
Open Research Questions

Pending editorial review.

Primary References

Pending editorial review.

Signature Section

The Perverse Incentive Lens™

How this behavior is exploited — and how to redesign around it.

Exploitation
Risk in user-facing AI products.
Amplifying Incentives
Risk in user-facing AI products.
Org Failure Modes
Risk in user-facing AI products.
Societal Failure Modes
Pending editorial review (HBT v1.0 auto-seed).
Ethical Considerations
Pending editorial review (HBT v1.0 auto-seed).
Redesign Strategies
Defense in depth. Continuous red-teaming. Don't rely on model alignment alone.
Diagnostic Questions
  • Defense in depth. Continuous red-teaming. Don't rely on model alignment alone.
Warning Signs

Pending editorial review.

Red Flags

Pending editorial review.

Intervention Playbook
Individual
Defense in depth. Continuous red-teaming. Don't rely on model alignment alone.
Team
Pending editorial review (HBT v1.0 auto-seed).
Organization
Pending editorial review (HBT v1.0 auto-seed).
Policy
Pending editorial review (HBT v1.0 auto-seed).
AI Implications
Detection
Pending editorial review (HBT v1.0 auto-seed).
Measurement
Pending editorial review (HBT v1.0 auto-seed).
Mitigation
Pending editorial review (HBT v1.0 auto-seed).
Responsible Use
Pending editorial review (HBT v1.0 auto-seed).

Interactive Mini Network

Click any neighbor to re-center the graph and follow the threads of connection.

HBT-INC-0165 · INC
Jailbreak
JaALAgentic LiabilityARAI Risk TieringBIBias in AI SystemsCTCounterfactual TestingDPDifferential PrivacyFMFairness MetricsFLFederated LearningPMProductivity MiragePIPrompt InjectionRARed-Teaming AI

Knowledge Graph Neighbors

Where Jailbreak is cited in the corpus

Questions about Jailbreak

What is Jailbreak?
Jailbreak is bypassing model safety constraints. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0165, within the Risk family. The core principle: bypassing model safety constraints. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
What is an example of Jailbreak?
Risk in user-facing AI products. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0165).
How is Jailbreak exploited?
Risk in user-facing AI products.
How do you design around Jailbreak?
Defense in depth. Continuous red-teaming. Don't rely on model alignment alone.
Which behavioral dimension does Jailbreak belong to?
Jailbreak is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Risk", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0165 and its evidence grade is C.

Version History

v1.1.0 · 2026-06-28Initial auto-seed from corpus.