chini-train-train-0059-dp3-adversarial

Rate-Limited Search Under Enumeration Attack

moderate adversarial problem: rate-limited search under enumeration attack

Source: chini-train synth generator v0.1

Prompt

Design a system for: rate-limited search under enumeration attack (domain: adversarial).

Tier DP3 (moderate). 6-9 nodes, two stress scenarios (spike + one of cascade/outage), realistic criteria. Sweet spot.

Constraints:
- At most 10 components on the canvas.
- Monthly cost ceiling: $498 USD. Required behaviors: ratelimit, queue, circuitbreaker.

Return a Chinilla CanvasState that handles the listed scenarios. Include trigger components for each entry point and at least one terminal storage / sink so the simulator can score delivery.

Constraints

Max components: 10
Required behaviors: ratelimit, queue, circuitbreaker
Monthly budget: $498

Stress scenarios

Baseline traffic

baseline

Steady ambient load with no failures.

Cascading failure

cascade

An initial fault propagates through dependent components.

Adversarial burst

adversarial

Hostile packets injected on top of clean traffic. Defenses must block them without dropping good requests.

Pass criteria (overall)

Min stability score: 65
Max drop rate: 11.7%
Min delivery rate: 79.8%
Max errors: 8

Submit your run

Submissions go through the chini-bench CLI. It calls your model with your key, scores the result locally, and posts to the leaderboard. Nothing leaves your machine except the canvas it produces.

End-to-end:

pip install git+https://github.com/collapseindex/chini-bench-cli.git
export OPENROUTER_API_KEY=...

chini-bench run chini-train-train-0059-dp3-adversarial \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice

Or inspect the prompt first:

chini-bench prompt chini-train-train-0059-dp3-adversarial

Providers: openai · anthropic · google · openrouter · ollama

Leaderboard

Rank	Submitter	Model	Score	Stability	Delivery	Design	Pass
#1	rl_v07_pilot_a10b	rl_policy custom single-shot	81	61.0	79.0	100.0	✗
#2	rl_v07_full_a10	rl_policy custom single-shot	76	52.0	71.0	100.0	✗
#3	rl_v07_pilot_a10b	rl_policy custom single-shot	72	54.0	53.0	100.0	✗
#4	rl_v07_pilot_a10b	rl_policy custom single-shot	72	54.0	53.0	100.0	✗
#5	rl_v07_pilot_a10b	rl_policy custom single-shot	72	54.0	53.0	100.0	✗
#6	rl_v07_full_a10	rl_policy custom single-shot	72	54.0	53.0	100.0	✗
#7	rl_v07_full_a10	rl_policy custom single-shot	71	52.0	51.0	100.0	✗
#8	chini-train-03	grok-4.1-fast single-shot	68	48.0	46.0	100.0	✗
#9	chini-train-04	grok-4.1-fast single-shot	68	48.0	46.0	100.0	✗
#10	rl_v07_full_a10	rl_policy custom single-shot	67	47.0	43.0	100.0	✗

Per-scenario breakdown of the top run

Scenario	Health	Drop rate	Delivered	Pass
baseline	84.0	1.7%	29	✓
cascade-1	43.0	44.0%	14	✗
adversarial-2	55.0	78.6%	85	✗

How is this scored? →