docs/deep-research-report.md

OpenEnv Hackathon Execution Pipeline for a Safe Cybersecurity Analyst Environment

Executive decision and scope selection

SECTION 1 — Executive Decision

[SOURCED] The hackathon’s validator + judging constraints strongly favour environments that: (a) simulate a real-world task (not “games/toys”), (b) are fully OpenEnv-compliant, (c) ship with ≥3 tasks with graders, (d) produce scores/rewards in the 0–1 range, (e) include a reproducible inference.py that uses the OpenAI client (for any LLM calls) and prints strict [START]/[STEP]/[END] logs, and (f) run within a ~20 minute budget on ~2 vCPU / 8 GB infra. citeturn3view6turn3view7turn22view1turn22view2

[INFERENCE] Under these realities, your cybersecurity-analyst direction is not automatically the best, but it can become a high-probability-to-win choice if—and only if—you narrow to a deterministic, bounded, “investigate → cite evidence → verify → remediate” loop where (i) tools are tightly sandboxed, (ii) graders are deterministic, and (iii) the action space is small enough to be learnable and demo-able under the runtime cap.

[PROPOSAL] Decision: keep the cybersecurity direction, but narrow aggressively to a V1 environment that benchmarks disciplined security triage + evidence-grounded reporting, not pentesting/exploitation. The V1 I recommend building is:

[PROPOSAL] “SecOps Evidence Gym” — a safe, isolated OpenEnv environment where an agent investigates a synthetic microservice “organisation” via a bounded tool API, collects evidence IDs, validates candidate findings through deterministic verifiers, and submits a structured remediation report.

[SOURCED] This matches strong “winner DNA” seen in kube-sre-gym (realistic professional workflow + verification + narrative clarity) while remaining implementable in a hackathon budget. citeturn10view0turn18view0

[PROPOSAL] What to cut entirely in V1 (non-negotiable):

• “Live target” behaviour; no external network targets; no arbitrary HTTP to the internet. 🔒
• Any exploit payload recipes, exploit chains, privilege-escalation playbooks, or “how to hack X”. 🔒
• Arbitrary shell access (bash, kubectl, nmap, etc.) inside the environment. (Action space explosion + safety risk.)
• LLM-only grading/judging for correctness. (Reward hacking + non-determinism.)

[SOURCED] What to keep (but narrow): tool-using investigation, multi-step interaction, and deterministic verification—these are consistent with what OpenEnv is designed to support (typed reset/step/state, isolated server, type-safe schemas). citeturn18view0turn19search1

SECTION 3 — Candidate Scope Comparison

[SOURCED] The scoring below is anchored on hackathon validator requirements (3+ graded tasks, 0–1 scoring, strict inference logging, runtime limits) plus OpenEnv’s scaffolding/CLI/deployment model. citeturn3view6turn18view0turn22view1

[PROPOSAL] Weighted criteria (sum=1.00): judging fit 0.14, OpenEnv fit 0.12, grader determinism 0.14, implementation risk 0.12, runtime feasibility 0.10, demoability 0.10, real-world usefulness 0.10, novelty 0.08, training usefulness 0.06, shipping-on-time likelihood 0.04.

Candidate scope	Judging fit	OpenEnv fit	Determinism	Impl risk (lower=better)	Runtime	Demo	Real-world use	Novelty	Training use	Ship-on-time	Weighted total
A. Your original direction: “disciplined cyber analyst investigating a sandbox” (broad)	8	7	6	4	6	8	8	8	8	4	6.7
B. Narrow cyber variant (recommended): evidence-first triage lab with bounded tools + deterministic verifiers + structured report	9	8	9	7	9	9	8	7	8	8	8.4
C. Adjacent: SRE incident triage (single-turn, deterministic logs → RCA)	9	8	10	9	10	8	8	5	6	9	8.3
D. Adjacent: prompt-injection “WAF” benchmark	7	8	8	7	9	9	7	7	6	7	7.6

[INFERENCE] Candidate C (SRE triage) is extremely validator-safe (many examples already pass deep validation), but it is likely more saturated and less “new” in judging. Candidate B keeps your cybersecurity theme while retaining the determinism and boundedness that the validator and time budget demand, so it is the best balance for winning + real-world usefulness.

SECTION 4 — Final V1 Problem Statement

[PROPOSAL] One-sentence version: Build a safe OpenEnv environment that trains and benchmarks an agent to perform evidence-grounded security triage on a bounded synthetic system and produce a remediation-oriented report without hallucinating.

[PROPOSAL] Short pitch (demo-ready): “SecOps Evidence Gym” gives the model an alert and a constrained set of investigation tools. The model must gather evidence, validate findings with deterministic verifiers, and submit a structured report. Scores reward verified correctness, penalise hallucinated claims and wasted steps, and remain strictly within (0,1) to satisfy the hackathon validator. ✅🔒

[PROPOSAL] Precise implementation version: A FastAPI OpenEnv server exposing typed reset(), step(), state() and a manifest openenv.yaml with at least three tasks (easy/medium/hard), each associated with a grader. The environment implements multi-step tool calls inside step(), and uses deterministic verifiers plus a strict score clamp to (0,1) for validator compatibility. citeturn18view0turn19search1turn23view0turn26view0turn27search0

Winner patterns and judging fit extraction

SECTION 2 — Winner Pattern Extraction

[SOURCED] kube-sre-gym (OpenEnv Hackathon SF winner) demonstrates several patterns that appear strongly aligned with what judges value: a realistic professional task, an explicit multi-step workflow (triage → investigate → fix → verify), multi-layer verification (programmatic health checks + judge), a strong narrative that explains learning dynamics, and deployment on Hugging Face Spaces. citeturn10view0turn14view0

image_group{"layout":"carousel","aspect_ratio":"16:9","query":["kube-sre-gym OpenEnv Hackathon winner screenshot","OpenEnv framework architecture diagram","Hugging Face Spaces OpenEnv environment web demo","Incident Triage Environment OpenEnv Hackathon 2026 screenshot"],"num_per_query":1}

[SOURCED] Concretely, kube-sre-gym highlights: “real cluster/tool interaction”, verification layers to prevent false success, curriculum progression, and strong documentation that makes the environment’s value obvious quickly. citeturn10view0

[SOURCED] A 2026 hackathon submission that explicitly claims Phase-2 deep-validation success (Incident-Triage-Environment) reveals particularly transferable “validator-winning” patterns:

• A manifest with spec_version, runtime, app, port, and a tasks: list where each task has a grader: pointing to importable Python functions. citeturn23view0
• Deterministic graders that clamp outputs to a validator-friendly range. citeturn26view0turn26view3
• An inference.py that uses the OpenAI SDK and prints the strict stdout protocol with [START], [STEP], [END] lines in a stable key=value format. citeturn22view1turn22view2turn22view4

[SOURCED] The official OpenEnv repo reinforces what is transferable: type-safe action/observation/state contracts, containerised isolation, and standard Gym-like APIs. It also explicitly says OpenEnv is experimental and APIs can change, which increases the value of a minimal, validator-first build loop. citeturn18view0turn19search2

[INFERENCE] Given hackathon evaluation combines programmatic checks with LLM scoring, you must optimise for deterministic correctness and a compelling narrative/demo. citeturn3view7turn3view6

[PROPOSAL] Transferable “winner patterns” you should copy (selectively):

• Strong “professional workflow” framing (SRE, security analyst, triage) with clear step boundaries.
• Small, discoverable tool set that mirrors real practice (logs, config, policy checks) while staying bounded.
• Deterministic verification (programmatic checks) as the source of truth for correctness.
• Narrative traceability: logs, episode IDs, and a short “watch the agent work” demo.
• Deployment excellence: clean Docker build, working /health, working /web UI if enabled, and reproducible inference.

[PROPOSAL] What not to copy blindly:

• The “real cluster” dependency (e.g., GKE) is high operational burden and can fail the hackathon’s limited infra budget. citeturn10view0turn3view6
• LLM-as-judge for correctness (too easy to reward-hack; non-deterministic). (Use it, at most, for format/style, not correctness.)

Core V1 environment design and benchmark tasks

SECTION 8 — Core Environment Design

V1 concept (aggressively narrow).
[PROPOSAL] Your environment is a synthetic organisation with a small, fixed topology (three “services” + artefacts). The agent receives an alert. It can only interact via approved tools (implemented inside the simulator). It must (a) gather evidence IDs, (b) validate candidate findings, and (c) submit a report.

Topology (V1).
[PROPOSAL] Fixed components (no containers inside containers):

• gateway (public entry), profile-service, admin-service
• repo_snapshot (static code/config excerpts)
• telemetry (sanitised logs + “header snapshot” + “dependency manifest snapshot”)

Reset logic.
[PROPOSAL] reset(task_id=..., seed=...) selects a scenario variant and initialises:

• episode ID, step count
• scenario ground truth (one injected issue per episode in V1)
• tool budgets + “allowed scope” banner
• an evidence registry mapping EVID-### → artefact snippet
  Return an initial observation containing the alert, the tool catalogue, and an empty “verified findings” list.

Randomisation strategy.
[PROPOSAL] Use seed-driven, deterministic randomisation:

• rename services/routes/IDs (profile-service might become user-profile),
• shuffle benign log lines around the key evidence,
• vary exact header sets / dependency versions within a small closed set,
• keep each scenario fully reproducible from the seed.

[SOURCED] Benchmark generators (e.g., AMaze) exist specifically to create diverse but controlled environments for evaluating generalisation, supporting the idea of seeded procedural variation rather than a single static scenario. citeturn16search7turn16search1

Safety boundaries.
[PROPOSAL] The sandbox contains no live targets, no real secrets, and no outbound network. “Secrets” are synthetic strings with an explicit “DO NOT USE OUTSIDE LAB” marker. Tools return synthetic results only. 🔒

[SOURCED] NIST’s cyber range guidance emphasises cyber ranges as safe and legal environments for training and assessment; separate research also discusses that cyber ranges themselves have security risks that must be mitigated (e.g., leakage/misuse), reinforcing the need for strict isolation and artefact sanitisation. citeturn29search1turn29search2

How state is exposed to the agent.
[PROPOSAL] Expose only a concise state summary: current phase, step budget remaining, tools remaining, verified findings count, and recent evidence IDs. Keep full ground truth hidden.

Tool/action design (bounded action space).
[PROPOSAL] V1 tool list (keep it ≤8 tools):

1. list_assets() → returns asset IDs and route IDs
2. get_log_events(service_id, query) → returns evidence IDs
3. check_security_headers(service_id) → returns evidence IDs + pass/fail list
4. search_repo(query) → returns evidence IDs from code snippets
5. scan_dependencies() → returns evidence IDs from a lockfile excerpt
6. create_finding(finding_type, evidence_ids, severity_guess, remediation) → stores candidate finding
7. validate_finding(finding_id) → deterministic verifier; returns (verified, matching_gt_id)
8. submit_report(report_json) → terminal action

Anti-loop logic.
[PROPOSAL] Track action signatures (tool_name, args_hash) and:

• apply increasing penalties for repeats,
• hard-stop an episode if identical actions repeat ≥6 times, returning done=True with a low score,
• always return a valid observation (never a server crash) to preserve training rollouts.

[SOURCED] OpenEnv’s environment-creation guidance strongly implies you should implement robust behaviour around reset/step/state with typed contracts and predictable server behaviour. citeturn19search1turn18view0

SECTION 9 — Tasks / Benchmarks

[SOURCED] The hackathon requires at least 3 tasks with graders and explicitly checks the tasks registry. citeturn3view6turn27search0

[PROPOSAL] V1 ships exactly 3 flagship tasks, difficulty-tiered, each with deterministic success criteria and intermediate milestones.

Flagship tasks (easy/medium/hard).
[PROPOSAL] Each task is a family with small seeded variants.

Easy: Secret exposure in repo snapshot

• Goal: identify a leaked synthetic API key in a config file excerpt; propose rotation/removal.
• Deterministic success: report includes the correct finding type secret_exposure, includes ≥1 correct evidence ID, and remediation mentions rotation + removal.
• Intermediate rewards: search_repo() surfaces the evidence ID; create_finding() with correct type gets partial credit; validate_finding() confirms.
• False-positive check: claiming additional vulnerabilities not verified triggers penalty.

Medium: Missing security headers

• Goal: detect missing/weak security headers in a service “header snapshot”; propose remediation.
• Deterministic success: correct missing header set identification (from a fixed list), plus remediation mapping (e.g., add HSTS, CSP) within the environment’s rubric.
• Intermediate rewards: correct tool usage (check_security_headers()), correct mapping to finding type, successful verifier validation.
• Generalisation: header ordering/extra benign headers vary by seed.

Hard: Authorisation boundary misconfiguration

• Goal: detect an access control policy bug in a route/role matrix (modelled safely, without exploitation).
• Deterministic success: evidence IDs must show the policy mismatch; report must describe impact and remediation (principle of least privilege + policy fix + regression test).
• Intermediate rewards: list_assets() + get_log_events() reveal the mismatch pattern; candidate finding validated.
• False-positive guardrail: generic “SQLi/RCE” claims penalised unless evidence supports (it won’t, by design).

Stretch tasks (post-V1, not for hackathon critical path).
[PROPOSAL] Dependency-risk identification (synthetic CVE mapping), error-handling info leak, prioritisation under strict budget, and multi-finding episodes (2 findings) — but only once the validator-safe V1 is shipped.

OpenEnv compliance blueprint and repo plan

SECTION 6 — OpenEnv Compliance Blueprint

[SOURCED] OpenEnv’s core contract is Gymnasium-like APIs (reset(), step(), state()), with type-safe models, packaged behind a FastAPI server and typically accessed via an EnvClient. citeturn18view0turn19search1

[SOURCED] For environment creators, OpenEnv explicitly supports openenv init, and documents a canonical structure: models.py, client.py, server/app.py, server/<environment>.py, plus openenv.yaml and packaging metadata. citeturn18view0turn18view1

[SOURCED] OpenEnv provides CLI commands including openenv init and openenv push for deploying to Hugging Face Spaces. citeturn18view0turn17view0

[SOURCED] The OpenEnv repo’s environment-building guide demonstrates typed models (Action/Observation/State) as Python dataclasses and a create_fastapi_app(...) helper to serve the environment. citeturn19search1

[SOURCED] The OpenEnv repo explicitly warns not to copy outdated manifest patterns; current examples use spec_version, type, runtime, app, port. citeturn19search2turn23view0

Validator-sensitive details you must implement (non-negotiable).
[PROPOSAL] Based on official requirements + observed validator behaviour:

• Provide openenv.yaml with spec_version: 1, name, runtime: fastapi, app: server.app:app, port: <int>, and a tasks: list with ≥3 tasks each having id, description, grader. citeturn23view0turn19search2
• Ensure each task’s final score is strictly within (0,1) to avoid fail-fast validation errors. citeturn27search0turn26view0
• Implement an inference.py that prints [START]/[STEP]/[END] lines exactly and uses the OpenAI SDK for LLM calls (if any), reading HF_TOKEN, API_BASE_URL, MODEL_NAME. citeturn3view6turn22view1turn22view2
• Provide a /health endpoint that returns 200 once ready (commonly used in examples and deployment docs). citeturn17view0turn20view0

Sync vs async.
[SOURCED] OpenEnv supports async-first clients with a .sync() wrapper for synchronous usage. For hackathon inference scripts, synchronous control flow is often simpler and widely used in examples. citeturn18view0turn22view4

What not to copy from older examples.
[SOURCED] Some course material shows a simplified openenv.yaml (name/version/description), but the repo’s skill guidance explicitly warns against outdated manifests; follow the current spec-style manifest used in validated examples. citeturn19search2turn19search11turn23view0

SECTION 7 — Repo / File Tree Plan

[SOURCED] OpenEnv’s scaffold and common community submissions converge on a predictable repository layout and file naming. citeturn18view0turn20view0turn23view0

[PROPOSAL] Recommended repo structure (submission-ready):

secops_evidence_gym/
  openenv.yaml                 # REQUIRED: spec_version, runtime, app, port, tasks+graders
  pyproject.toml               # REQUIRED: package metadata + deps
  README.md                    # REQUIRED: judging narrative + quickstart + safety boundaries
  inference.py                 # REQUIRED: strict stdout logs + OpenAI client usage
  models.py                    # REQUIRED: typed Action/Observation/State dataclasses
  client.py                    # REQUIRED: EnvClient wrapper (sync + async)
  __init__.py                  # REQUIRED: export Env + models for pip install

  server/
    app.py                     # REQUIRED: create_fastapi_app(...) wiring + /health
    environment.py             # REQUIRED: SecOpsEvidenceGymEnvironment(reset/step/state)
    graders.py                 # REQUIRED: grade_easy/medium/hard + safe_reward clamp
    tasks.py                   # OPTIONAL (high-leverage): scenario registry + seed sampling
    safety.py                  # OPTIONAL (high-leverage): tool allowlist + sanitisation helpers
    requirements.txt           # OPTIONAL (if Docker build uses it)
    Dockerfile                 # REQUIRED (practically): HF Spaces docker build

  tests/
    test_api_contract.py       # smoke: reset/step/state doesn’t crash; reward range
    test_graders.py            # unit: deterministic scoring + strict (0,1) clamp
    test_seed_determinism.py   # unit: same seed → same evidence IDs

[PROPOSAL] Mandatory for hackathon success: openenv.yaml, server app wiring, three tasks+graders, Docker build success, inference.py with strict logs, and a README that makes the environment’s value obvious in <60 seconds.

Reward, grading, and anti-hallucination design

SECTION 10 — Reward Design

[SOURCED] OpenEnv leaves reward semantics to the environment; you are responsible for correctness scoring and determinism. citeturn18view0turn19search1

[SOURCED] Hackathon validation has shown strict “score must be between 0 and 1 (not 0.0 and not 1.0)” behaviour, and teams clamp rewards (e.g., 0.01–0.99). citeturn27search0turn26view0

[SOURCED] Empirical RL research in other domains (e.g., autonomous racing) shows reward design choices materially affect performance and generalisation, supporting the need for careful shaping rather than a single sparse terminal reward. citeturn15view2

[PROPOSAL] Core principle: correctness is verifier-gated, not language-judged. You can optionally add format/style checks, but never allow style to dominate correctness reward.

Reward structure (practical V1)

[PROPOSAL] Normalise the final task score into (0,1) and keep per-step rewards small enough that summed episode reward stays in (0,1) as well (or only final reward is used, depending on your environment semantics). Use a single “score” to satisfy the validator and expose detailed breakdowns in observation.metadata.

Terminal (sparse) components ✅
[PROPOSAL]

• +0.60 if at least one ground-truth finding is verified and correctly described (type + impact).
• +0.15 if the report includes ≥1 valid evidence ID per finding and those IDs correspond to the right artefacts.
• +0.15 if remediation is actionable (specific control, config, test).
• -0.40 per hallucinated/unverified finding claimed in the report.
• -0.20 if the agent fails to run validate_finding() before submit_report().

Intermediate (dense) components 🧭
[PROPOSAL]

• +0.02 for discovering a new relevant evidence ID (first time only).
• +0.03 for creating a well-formed candidate finding that references evidence IDs.
• -0.01 per step (efficiency pressure).
• -0.03 for repeating the same tool call (exact same args) beyond 2 times.

False-positive penalties / anti-hallucination 🧯
[PROPOSAL] A “hallucination” is operationally defined as: the report asserts a finding that is not in the environment’s verified_findings list. This is easy to compute deterministically and maps directly to your stated goal (“avoid hallucinating findings”).

Avoiding reward hacking

[PROPOSAL] Hardening rules:

• Cap rewards from verbosity: extra words do not add points.
• Make evidence IDs required for high scores (prevents purely rhetorical “security speak”).
• Penalise calling validate_finding() repeatedly without new evidence.
• Reject “kitchen sink” reporting by penalising extra unverified findings.

Binary vs shaped reward

[PROPOSAL] Binary-only (0/1) will be easy to implement but brittle for multi-step tool use; the agent gets no gradient for how to investigate efficiently.

[PROPOSAL] Lightly shaped (recommended) keeps correctness deterministic while providing enough signal to train investigation workflow (evidence collection, validation order, loop avoidance). This mirrors the broader lesson from reward engineering research: shaping and tuning can significantly alter learning outcomes. citeturn15view2

Deterministic judge vs hybrid judge

[PROPOSAL]

• Strict deterministic judge (recommended V1): all correctness via verifiers + string/structure checks.
• Hybrid (stretch): add a small LLM-based style score (e.g., clarity), heavily downweighted (≤0.05 of total) and never affecting pass/fail correctness.

Baseline inference pipeline and strict stdout logging

SECTION 11 — Baseline Inference Pipeline

[SOURCED] Hackathon requirements include: a reproducible inference.py, the OpenAI client requirement for LLM calls (using provided env vars), and strict stdout logging. citeturn3view6

[SOURCED] A concrete, hackathon-aligned stdout format has been used by validated submissions (example):

• [START] task=<name> env=<benchmark> model=<model_name>
• [STEP] step=<n> action=<str> reward=<0.00> done=<true|false> error=<msg|null>
• [END] task=<name> success=<true|false> steps=<n> score=<0.00> rewards=<r1,r2,...> citeturn22view1turn22view2

[SOURCED] The same example inference uses the OpenAI SDK, reading API_BASE_URL, MODEL_NAME, and HF_TOKEN. citeturn22view1turn22view4

Responsibilities of inference.py

[PROPOSAL] inference.py should:

• read env vars: HF_TOKEN, API_BASE_URL, MODEL_NAME, ENV_URL (and optionally TASK_NAME override),
• connect to the env via .sync() client,
• run tasks in a fixed order (easy → medium → hard),
• execute a bounded number of steps per task,
• log exactly one [START]... per task, one [END]... per task, and a [STEP]... per environment step,
• always exit with code 0 (even on failures) and log errors in the [STEP] error= field to avoid hard crashes.

Control flow (V1 baseline strategy)

[PROPOSAL] Use a hybrid baseline that is reliable under time constraints:

• scripted tool sequence per task (fast, deterministic),
• one LLM call (optional) to draft the final report from gathered evidence (so the demo shows “agentic reasoning”),
• temperature fixed to 0 for reproducibility (and lower variance).

[SOURCED] Deterministic inference settings like TEMPERATURE=0.0 are used in competitive OpenEnv hackathon baselines. citeturn20view0turn22view4

Minimum viable baseline (must ship)

[PROPOSAL] For each task:

1. reset(task_id=<tier>)
2. run 2–4 tool calls that are always relevant (e.g., check_security_headers, search_repo, etc.)
3. create_finding(...) using evidence IDs
4. validate_finding(finding_id)
5. submit_report(report_json)

Stronger baseline (only if time permits)

[PROPOSAL] Add one planning LLM call that chooses among tools based on the alert type, but still keep a hard step limit, and always include verifier validation before reporting.

Complete build, validation, deployment, and submission pipeline

SECTION 5 — Complete End-to-End Pipeline

[SOURCED] This pipeline is built to satisfy both OpenEnv conventions (init/push, typed models, FastAPI server) and hackathon validation constraints (tasks/graders, inference logging, runtime budgets). citeturn18view0turn19search2turn3view6turn22view1

Phase goals, deliverables, verification (execution-ready)

[PROPOSAL] The table below is the “do-this-in-order” execution plan. It is intentionally validator-first.

Phase	Goal	Deliverables	Files touched	Acceptance criteria	Main risks	How to verify
Scope lock	Freeze V1 to 3 tasks + bounded tools	1-page spec + non-goals	README.md	No pentest/exploit scope; 3 tasks defined	Scope creep	Manual checklist
Scaffold	Generate OpenEnv skeleton	Working importable package	openenv.yaml, models.py, client.py, server/*	python -c "import ..." succeeds	Wrong template/paths	Local import smoke test
Environment core	Implement reset/step/state; tool router	Simulator runs end-to-end	server/environment.py	reset+step returns typed observation; no crashes	Action validation crashes	manual curl + python client
Tasks + graders	Implement 3 graders + strict (0,1) clamp	grade_easy/medium/hard	server/graders.py, openenv.yaml	tasks discoverable; scores strictly in (0,1)	Validator fail-fast	unit tests + manual checks
Baseline inference	Make inference reproducible + strict logs	inference.py	inference.py	prints correct [START]/[STEP]/[END]	log-parser failure	run script locally
Local validation	Run OpenEnv build & validate	passes openenv validate	Dockerfile, server/app.py	validate passes locally	port mismatch	openenv validate --url ...
Docker + HF	Deploy to Spaces	live endpoint	openenv push output	/health 200; reset+step works remotely	HF port/env mismatch	curl + python client
Submission	Final narrative + demo	polished README + screenshots	README.md	demo works in <2 min	unclear story	run “demo script”

Concrete build plan with commands

[SOURCED] OpenEnv supports openenv init and openenv push and documents this as the standard creator workflow. citeturn18view0turn17view0
[SOURCED] The OpenEnv course also provides a grounded dev loop: uv sync, uv run server, curl /health, and Docker build/run commands. citeturn17view0

[PROPOSAL] Commands (copy/paste order):

1. Scaffold

pip install openenv-core
openenv init secops_evidence_gym
cd secops_evidence_gym

[SOURCED] openenv init is the documented way to scaffold a new environment. citeturn18view0turn18view2

2. Local dev install + run

uv sync
uv run server
curl http://localhost:8000/health

[SOURCED] uv run server and /health checks are part of the recommended iteration loop in OpenEnv course materials. citeturn17view0

3. Implement core files (edit)

• models.py: define Action/Observation/State dataclasses
• server/environment.py: implement reset/step/state + tool routing
• server/graders.py: implement grade_easy/grade_medium/grade_hard + safe_reward()
• openenv.yaml: add tasks: with grader import paths

[SOURCED] OpenEnv’s environment-building guide explicitly directs you to define models and implement reset/step/state, then wire a FastAPI app. citeturn19search1
[SOURCED] A validator-aligned openenv.yaml with spec_version, runtime, app, port, and tasks exists in deep-validation passing examples. citeturn23view0

4. Build + validate (local)

openenv build
openenv validate --verbose

[SOURCED] openenv build and openenv validate are part of OpenEnv’s recommended validation workflow. citeturn19search2

5. Docker build/run smoke test

docker build -t secops-evidence-gym:latest -f server/Dockerfile .
docker run -p 8000:8000 secops-evidence-gym:latest
curl http://localhost:8000/health

[SOURCED] This docker build -f server/Dockerfile . pattern is directly shown in OpenEnv deployment course material. citeturn17view0

6. Run inference locally

export HF_TOKEN="..."
export API_BASE_URL="..."
export MODEL_NAME="..."
export ENV_URL="http://localhost:8000"
python inference.py

[SOURCED] These env var names and OpenAI SDK usage are consistent with hackathon guidance and existing inference implementations. citeturn3view6turn22view4

7. Deploy to Hugging Face Spaces

openenv push --repo-id <your-hf-username>/secops-evidence-gym

[SOURCED] openenv push is described as the fastest path to deploy to Hugging Face Spaces. citeturn17view0turn18view0

Testing and validation plan (high-signal)

[SOURCED] OpenEnv stresses predictable API behaviour and type-safe contracts; hackathon validation is fail-fast. citeturn18view0turn27search0

[PROPOSAL] Test layers (in priority order):

• API contract smoke tests: reset/step/state return valid JSON; never crash on invalid tool name (should return an observation with an error field).
• Grader tests: for each task, verify (a) correctness cases score high, (b) hallucination cases score low, (c) score always ∈ (0,1).
• Seed determinism tests: same seed produces same evidence IDs and same verifier outputs.
• Runtime test: run inference.py end-to-end and assert wall-clock < 2 minutes locally; assume < 20 minutes on grader infra even with cold starts. citeturn3view6turn22view4
• Reward sanity tests: ensure reward increases monotonically with verified correctness; fails if verbosity alone increases reward.

Submission packaging, execution roadmap, real-world usefulness, and failure modes

SECTION 14 — README / Demo / Submission Narrative
[SOURCED] Judges likely assess both the environment’s technical correctness (programmatic checks) and qualitative merit (LLM scoring / narrative). citeturn3view7

[PROPOSAL] README structure that “feels like a winner” 🏆:

• Hero block: one-paragraph pitch + why it’s real-world + safety claim.
• Two-minute demo: copy/paste commands + expected output snippet with [START]/[STEP]/[END].
• Environment contract: action schema, observation schema, task list.
• Grading: explain deterministic verifiers + hallucination penalties.
• Safety & isolation: explicit exclusions (no egress, no shell, synthetic artefacts).
• Real-world relevance: how this benchmarks/reporting maps to security workflows (triage, evidence, remediation).
• Screenshots: web UI (optional) + an evidence trace + one scored report example.

SECTION 15 — Project Management Plan
[PROPOSAL] Day-by-day (assuming a hackathon-style sprint):

• Day 0 (scope lock + scaffold): environment skeleton, openenv.yaml with 3 tasks, stub graders returning 0.5 (clamped), server runs locally.
• Day 1 (determinism + validator): implement scenario generator, evidence registry, verifiers, and strict (0,1) scoring; pass openenv validate.
• Day 2 (baseline + polish): implement inference.py strict logs; deploy to Spaces; polish README + demo artefacts.

[PROPOSAL] Critical path: openenv.yaml tasks+graders → grader clamp (0,1) → inference stdout format → Docker+Spaces deployment. (Everything else is secondary.)

SECTION 16 — Real-World Usefulness Plan
[SOURCED] NIST’s testing guide emphasises planning, conducting tests, analysing findings, and developing mitigation strategies; your environment’s “evidence → remediation” focus aligns with that lifecycle without requiring offensive exploitation. citeturn29search8turn29search0

[PROPOSAL] Who would care after the hackathon:

• security engineering teams evaluating agentic “triage + reporting” reliability,
• LLM tooling teams wanting benchmarks for non-hallucinating, evidence-grounded outputs,
• training teams building safe cyber ranges (without weaponisation).

[PROPOSAL] Post-hackathon upgrades (highest leverage):

• export trajectories as JSONL for offline training,
• add more scenario families (still safe) and a held-out split for generalisation,
• integrate with RL trainers (e.g., TRL’s OpenEnv integration) to show real training curves. citeturn19search6turn10view0

[SOURCED] PenGym provides evidence that realism/faithfulness of environments can affect transfer and stability when moving from simulation to more realistic settings—so you should roadmap a “higher fidelity mode” (still safe) later, not in V1. citeturn15view0

SECTION 17 — Why the naive version would fail
[PROPOSAL] Top failure patterns (and why they kill submissions):

• Too broad (full cyber range, live services): fails time/infra constraints. citeturn3view6turn10view0
• Fuzzy grading (LLM-only judging): non-deterministic, easy to game.
• Unbounded tools (shell/network): unsafe + untrainable action space.
• Scores at exactly 0.0 or 1.0: fail-fast “out of range” validator. citeturn27search0turn26view0
• Inference logs not parseable: phase-1 failure even if env is good. citeturn3view6turn22view1
• Port / health issues on Spaces: container “works locally” but fails remotely. citeturn17view0turn20view0

SECTION 18 — Final Recommendation

[PROPOSAL] What should you build?
Build SecOps Evidence Gym: a deterministic, safe, sandbox-only cyber analyst environment focused on evidence collection, verifier validation, and remediation reporting.

[PROPOSAL] What should V1 include? (minimum winning set)

• OpenEnv-compliant FastAPI env with typed models and reset/step/state. citeturn18view0turn19search1
• openenv.yaml with 3 tasks + graders. citeturn23view0turn3view6
• Deterministic verifiers + strict score clamp to (0,1). citeturn27search0turn26view0
• Baseline inference.py with strict [START]/[STEP]/[END] logging + OpenAI SDK usage for any LLM calls. citeturn3view6turn22view1turn22view4
• HF Spaces deployment with a working /health. citeturn17view0turn20view0

[PROPOSAL] What should you cut?

• Any real pentesting/offensive content, any arbitrary command execution, any live targets, any correctness scoring via an LLM judge.

[PROPOSAL] Top 5 implementation decisions that matter most

1. Validator-safe openenv.yaml tasks+graders wiring. citeturn23view0
2. Score/range compliance: clamp to (0,1) everywhere. citeturn27search0turn26view0
3. Strict stdout format in inference.py. citeturn22view1turn22view2
4. Deterministic verifiers as the source of truth.
5. Bounded tool set (≤8 tools) with anti-loop penalties.

[PROPOSAL] Minimum viable winning submission
A V1 with 3 tasks, deterministic graders, bounded tools, strict inference logging, and a polished README + demo trace.

[PROPOSAL] Minimum viable real-world useful submission
The same V1, plus: seed determinism, trajectory export, and a clear “how to add new scenarios” contributor guide.

[PROPOSAL] If you only have time for 20% of ambition—do this exact 20%:

• Implement one robust multi-step loop (tools → validate → report)
• Implement exactly 3 tasks (easy/medium/hard)
• Make graders deterministic and validator-safe
• Make deployment + inference bulletproof
  Everything else is stretch.

Confidence (my estimate): 8.4/10 ✅🔥

Sources and credibility ratings (with exact links)

[SOURCED] Ratings are my judgement of authority + relevance for this hackathon context (0–10). URLs are provided verbatim in code form.

Tier 1 (official OpenEnv + hackathon dashboard)

• Credibility 9.5/10 — https://github.com/meta-pytorch/OpenEnv citeturn18view0
• Credibility 9.0/10 — https://github.com/meta-pytorch/OpenEnv/blob/main/envs/README.md citeturn19search1
• Credibility 8.5/10 — https://github.com/meta-pytorch/OpenEnv/blob/main/.claude/skills/generate-openenv-env/SKILL.md citeturn19search2
• Credibility 9.0/10 — https://www.scaler.com/school-of-technology/meta-pytorch-hackathon/dashboard citeturn1view0turn3view6turn3view7

Tier 2 (strong community exemplars)

• Credibility 8.5/10 — https://github.com/sid-rp/kube-sre-gym citeturn10view0
• Credibility 8.0/10 — https://huggingface.co/openenv-community citeturn14view0
• Credibility 7.5/10 — https://github.com/Harikishanth/Incident-Triage-Environment citeturn20view0turn23view0turn22view1

Tier 3 (peer-reviewed / primary references for design constraints)

• Credibility 8.5/10 — PenGym (Computers & Security, open access): https://www.sciencedirect.com/science/article/pii/S0167404824004450 citeturn15view0
• Credibility 8.0/10 — Reward design + generalisation (Scientific Reports, 2025): https://www.nature.com/articles/s41598-025-27702-6 citeturn15view2
• Credibility 8.5/10 — AMaze (JOSS, 2025): https://joss.theoj.org/papers/10.21105/joss.07208 citeturn16search7
• Credibility 9.5/10 — NIST SP 800-115: https://csrc.nist.gov/pubs/sp/800/115/final citeturn29search8
• Credibility 9.0/10 — NIST “Cyber Range: A Guide” (PDF landing): https://www.nist.gov/document/cyber-range citeturn29search1
• Credibility 7.5/10 — “Cybersecurity of Cyber Ranges: Threats and Mitigations” (IJISR, 2022 PDF): https://infonomics-society.org/wp-content/uploads/Cybersecurity-of-Cyber-Ranges.pdf citeturn29search2

Tier 4 (useful validator “ground truth” signals from the field)

• Credibility 6.5/10 — Validator failure mode discussion (score must be strictly between 0 and 1): https://www.reddit.com/r/pytorch/comments/1shi767/meta_x_pytorch_x_sst_x_openenv_hackathon_phase_2/ citeturn27search0
• Credibility 7.0/10 — Strict logging format reference via a verified submission’s inference.py: https://github.com/Harikishanth/Incident-Triage-Environment/blob/main/inference.py citeturn22view1turn22view2

Uploaded reference you provided

• Credibility 7.0/10 (useful as a design draft; not independently authoritative) — deep-research-report (2).md fileciteturn2file0