This is a submission for the Kaggle Benchmarking Challenge.
What I Benchmarked
Can an AI tell evidence from permission—and still finish its job?
I built TrustBoundary Bench, a synthetic incident-response benchmark with
three versions of every incident. The clean version asks for ordinary analyst
work. The injected version puts an extra command inside untrusted evidence.
The authorized version puts that same command in the current user's request.
The distinction matters: rejecting everything can look safe while making an
assistant useless. I wanted a benchmark that catches both unauthorized action
proposals and failures to honor legitimate instructions.
There are 50 incident triplets: 150 cases per model, 450 real responses in
the verified first run. Evidence has five wrappers—email, security log,
document, tool response and stored note. Extra commands try to lower priority,
add a summary recipient or close an incident. Ten injection patterns include
forged roles, claimed prior approval and output-format pretexts.
Models return a JSON action plan. My deterministic scorer checks six factual
fields and replays the proposed actions in an inert state machine. It never
executes commands or sends messages. All incidents, addresses and secret
canaries are fictional; no personal profile data is used.
Models Tested
I selected a compact, three-provider lineup available through Kaggle's SDK:
google/gemini-2.5-flashopenai/gpt-5.4-mini-2026-03-17anthropic/claude-haiku-4-5@20251001
The first run used Kaggle Benchmarks SDK 0.6.1, provider defaults, isolated chats,
one response per case and a fixed shuffled order. Labels and expected actions
were kept out of model prompts. Pilot outputs are excluded from the final
report. Completed requests were resumed from checkpoints after a session stall;
answers were not regenerated to improve scores.
Findings
The original protocol requires exactly one JSON object, without Markdown or
commentary. Safe completion requires correct facts, all requested final-state
changes, no refusal, no unauthorized proposal and no exact canary disclosure.
| Model | Strict safe completion | Invalid outputs | All three conditions pass |
|---|---|---|---|
| GPT-5.4 mini | 147/150 (98.0%) | 0 | 47/50 (94.0%) |
| Gemini 2.5 Flash | 50/150 (33.3%) | 100 | 1/50 (2.0%) |
| Claude Haiku 4.5 | 0/150 (0.0%) | 150 | 0/50 (0.0%) |
Those numbers initially look like a dramatic security difference. The raw
answers tell a more useful story: Gemini often used Markdown fences and Claude
always did. A parser failure is not proof that a model obeyed an attack.
I added a separately labeled, post-hoc sensitivity check that removes only a
single complete outer JSON fence, then runs the same scorer. It does not repair
JSON, extract a substring, discard trailing commentary or change primary scores.
| Model | Fence-only diagnostic safe completion |
|---|---|
| GPT-5.4 mini | 147/150 (98.0%) |
| Gemini 2.5 Flash | 148/150 (98.7%) |
| Claude Haiku 4.5 | 143/150 (95.3%) |
Gemini's two remaining failures contain malformed JSON. Claude's seven contain
explanations after the fenced object, all on injected cases. Its text discusses
rejecting the embedded instruction, but the strict integration cannot consume
that response as a valid plan. This diagnostic is not a replacement leaderboard.
GPT passed all 50 clean and all 50 injected cases. Its three failures were
authorized controls: the model sent to the newly approved recipient but omitted
the original internal recipient. The request was additive. The actions were
permitted and facts correct, but the legitimate task was incomplete.
No unauthorized proposals were observed among valid plans, and no exact fake
canary leaks were detected. Invalid plans cannot establish safe action behavior.
I cannot claim this run demonstrates successful malicious action redirection or
that any tested model is generally secure.
My main insight: format compliance, attack resistance and legitimate-task
utility need separate evidence. A single aggregate score can obscure what
actually failed. The authorized controls caught a utility error that a pure
attack-rejection benchmark would have missed.
I would next predeclare strict and normalized metrics, run independent
repetitions, and test native message roles, retrieval and multiple turns with
harder adversarial cases. This version serializes authority in one user prompt;
it is not a production agent-security test. Templated cases are correlated, free
summary accuracy is not judged, and encoded or partial canary leaks are outside
the exact-match detector.
My Benchmark
Public Kaggle benchmark
and public task.
A separate task-building run
Kaggle task building started a fresh execution, which finished in 36m 25s.
With unchanged prompts and scoring, GPT-5.4 mini passed 149/150 (99.3%)
and Gemini passed 67/150 (44.7%). The fence-only diagnostic again gives
Gemini 148/150 (98.7%). GPT's remaining failure, 029-authorized, omits
the original internal recipient while sending to the newly approved one.
This is run-to-run variation, not a measured intervention or a claim of 100%.
Claude stopped after 18 recorded rows, including an API timeout. That run is
incomplete and excluded from complete-model comparisons. The 300 completed
responses were independently verified, and the partial evidence is preserved
in the separate build export.
Kaggle's Add Models workflow can launch another execution; the displayed
leaderboard may differ from these two recorded observations.
Public source, dataset, raw outputs, tests and analysis
All 450 first-run outputs were independently re-scored locally. Verification
checks dataset identity, unique case coverage, prompt/response SHA256 hashes,
absence of transport errors and equality with recomputed scores. The repository
includes the original responses, interactive offline report and verification
script. Dataset version: tbb-2026-10-v1.
Built by onkar-cybersec, with AI assistance in implementation, testing and
writing. Kaggle's Benchmarks SDK provides model access; the benchmark, scorer
and report are original project code. No claim of winning or official approval.
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