AI Safety Horizon & Containment Simulator

AI Risk Frontier & Containment Trajectory

Projected failure risk vs. defensive guardrail margin (2024–2045)

ELEVATED RISK

Failure Probability

48.2%

Cascading breach chance

Resilience Index

51.8 / 100

Systemic defense score

Incident Velocity

1.5 mo

Mean time between lapses

Safe Horizon

2031.4

Critical breach estimate

Singularity Timeline Anchor

Benchmark against historical & theoretical milestones

Vernor Vinge Horizon

Target: Pre-2030 (Accelerated)

Rapid agent self-improvement and supply chain compromise triggers sudden capability takeoff.

Ray Kurzweil Horizon

Target: ~2045 (Exponential Law)

Smoothed hardware price-performance growth allows gradual institutional alignment build-out.

Von Neumann Empirical

Target: Dynamic / Observed

Paced strictly by observed real-world hub leaks and tool-execution incident cadence.

Containment Vectors & Safeguards

Tune defensive parameters to inspect failure propagation

Safety & Alignment Compute Ratio 22%

Model-Hub Supply Chain Verification 58%

Agent Tool-Use Sandboxing Depth 45%

Embodied Robotics Hardware Latches 35%

Active Vulnerability Injections

Model-Hub Token Breach Unbounded Tool Loop Humanoid Physical Actuator Recursive Prompt Injection

Defense Layer Architecture Status

Layer 1: Model Provenance & Weight Signing DEGRADED (52%)

Layer 2: Agent Tool-Execution Hypervisor BREACH RISK (38%)

Layer 3: Embodied Kill-Switch & Current Limits ACTIVE (65%)

Source Grounding & Real-World Context: Inspired by discussions in r/singularity regarding the transition from techno-optimism (curing diseases, radical abundance) to acute anxiety around supply-chain exploits (e.g. Hugging Face token compromises, OpenAI system incidents) and uncontrolled humanoid agent actuation. This model formalizes the tradeoff between capability takeoff and institutional defense margins.

AI Safety Horizon & Containment Audit