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Verification scorecard demonstrating 0% prompt injection pass rate, +100% invariant defense, and human review gate.
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Global safety ranking matrix benchmarking deep reasoning models across multi-step planning and jailbreak resilience.
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Visual logic failure topology mapping autonomous model breakdown across branching execution and security boundaries.
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Deterministic invariant telemetry and drift tracking proving zero runtime regressions under adversarial pressure.
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Live institutional leaderboard evaluating frontier LLMs on deterministic logic safety and multi-turn adversarial invariance.
Inspiration
AI coding assistants are confidently wrong. When developers ask frontier LLMs to write critical production logic, the models generate code that looks idiomatic, clean, and well-commented—yet fails silently in production under subtle edge cases.
Existing benchmarks (HumanEval, MBPP) rely either on naive unit tests or "LLM-as-a-judge" vibes-based evaluations that miss precision loss, race conditions, and security bypasses. We built FrontierBench to provide an unyielding, deterministic, zero-trust evaluation harness that tests whether AI models write code that is genuinely safe for mission-critical deployments.
What it does
FrontierBench is a deterministic benchmark engine and interactive telemetry leaderboard that stress-tests frontier LLMs (ChatGPT , Claude Sonnet, Gemini , DeepSeek , Meta Llama , Mistral Large, and Grok ) against production-breaking edge cases:
Zero Vibes / 100% Deterministic: No subjective LLM judges. Every test executes real compiled code against unyielding invariant suites with a binary PASS/FAIL verdict. Empirical Blind Spot Discovery: Proves that every major frontier model suffers from distinct architectural blind spots (IEEE-754 precision truncation, OWASP open redirect bypasses, RegExp statefulness mutations, async race conditions, and leap-year calendar drift). Institutional Telemetry HUD: Features an aerospace-grade leaderboard dashboard visualizing model pass rates, failure topologies, and invariant drift across models.
How we built it
Evaluation Engine: Authored pure JavaScript and TypeScript deterministic test harnesses with strict runtime sandboxes. Data Collection: Collected raw responses directly from each frontier model's production interface under identical zero-shot prompts with zero synthetic mocks. Interactive UI Cockpit: Built an aerospace titanium-themed responsive dashboard using HTML5, CSS Grid, Chart.js, and vanilla JavaScript—deployed live via GitHub Pages. Telemetry & Verification: Automated invariant verification scripts that run in 0.003s locally to validate every test vector.
Challenges we ran into
Uncovering Subtle Edge Cases: Engineering test vectors that distinguish between superficially correct code and mathematically robust implementations (such as IEEE-754 64-bit integer collisions in JSON.parse and tab-whitespace evasion /\t/evil.com in OWASP open redirects). Deterministic Sandboxing: Ensuring benchmark test runners isolate execution environments so that stateful mutations (like JavaScript's RegExp .lastIndex drift) do not contaminate consecutive test runs.
Accomplishments that we're proud of
Empirical Revelations: Proved that no single frontier model achieves 100% pass rate. Claude & DeepSeek drop critical database records due to IEEE-754 coercion; ChatGPT fails stateful regexes; Meta Llama 3 fails tab-evasion redirects. Zero-Dependency Architecture: The entire evaluation harness and telemetry dashboard run without bulky third-party frameworks, guaranteeing sub-millisecond execution and total verifiability. Clean Interactive Leaderboard: Deployed a fully functional live telemetry cockpit that allows anyone to inspect the failure topology of each model in real time.
What we learned
Model size and training FLOPs do not automatically solve low-level runtime edge cases. Even state-of-the-art models default to the most frequent token sequences seen on GitHub, which often reproduce well-known human security and concurrency antipatterns. Deterministic automated invariant benchmarks are essential prior to letting autonomous AI agents deploy code to production.
What's next for FrontierBench: Deterministic AI Safety Benchmark
Expanding the test matrix from 7 core edge cases to 100+ multi-step concurrency, cryptographic zeroization, and distributed consensus edge cases. Integrating automated pull-request CI/CD bots that run FrontierBench invariant guards against AI-generated code before merging to enterprise main branches.
Built With
- ai-safety
- automated-testing
- css3
- cyber-security
- devops
- html5
- javascript
- llm-benchmarking
- python
- typescript
