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chini-train-train-0144-dp4-adversarial

Support-Chatbot Under Prompt-Injection Attempts

hard adversarial problem: support-chatbot under prompt-injection attempts

Source: chini-train synth generator v0.1

Prompt

Design a system for: support-chatbot under prompt-injection attempts (domain: adversarial).

Tier DP4 (hard). 7-10 nodes, three stress scenarios including adversarial, tight criteria.

Constraints:
- At most 11 components on the canvas.
- Monthly cost ceiling: $415 USD. Required behaviors: ratelimit, queue, circuitbreaker, retry.

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
11
Required behaviors
ratelimit, queue, circuitbreaker, retry
Monthly budget
$415

Stress scenarios

Baseline traffic

baseline

Steady ambient load with no failures.

Traffic spike

spike

Traffic suddenly multiplies. The hot path must hold.

Adversarial burst

adversarial

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

Dependency outage

outage

A downstream component is disabled. System must degrade gracefully.

Pass criteria (overall)

Min stability score
72
Max drop rate
9.5%
Min delivery rate
83.9%
Max errors
7

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-0144-dp4-adversarial \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice
Or inspect the prompt first:
chini-bench prompt chini-train-train-0144-dp4-adversarial
Providers: openai · anthropic · google · openrouter · ollama

Leaderboard

Rank Submitter Model Score Stability Delivery Design Pass
#1 rl_v07_full_a10
rl_policy
custom single-shot
88 71.0 92.0 100.0
#2 chini-train-03
opus-4.7
single-shot
81 56.0 97.0 85.0
#3 rl_v07_full_a10
rl_policy
custom single-shot
79 64.0 66.0 100.0
#4 rl_v07_full_a10
rl_policy
custom single-shot
78 63.0 64.0 100.0
#5 rl_v07_full_a10
rl_policy
custom single-shot
70 55.0 42.0 100.0
#6 chini-train-03
grok-4.1-fast
single-shot
68 51.0 39.0 100.0
#7 chini-train-04
grok-4.1-fast
single-shot
68 51.0 39.0 100.0
Per-scenario breakdown of the top run
Scenario Health Drop rate Delivered Pass
baseline 82.0 1.7% 29
spike-1 63.0 16.1% 1040
adversarial-2 55.0 77.9% 105
outage-3 83.0 1.4% 35