| # binary-reasoning-v1 — Datasheet | |
| **Product:** verified reverse-engineering tasks (dataset + RL/eval environment) over real binaries. | |
| **Seller:** Samuel Anttila / RE-Lab (Rovaniemi, FI). Contact: samueljanttila@gmail.com | |
| **License:** (c) Samuel Anttila / RE-Lab. Free for evaluation & research; commercial license on purchase. | |
| ## Composition (10,251 items, unique IDs, 2026-10-29) | |
| | family | tier | grading | count | | |
| |---|---|---|---| | |
| | func_id_exact — identify internal/stripped function from scrubbed disassembly | hard/med | deterministic symbol match (alias-tolerant) | 9,073 | | |
| | func_id_mc — multiple-choice function identification | medium | deterministic option match | 1,036 | | |
| | func_summary — summarize scrubbed function (purpose + identifying detail) | med/hard | rubric LLM judge | 53 | | |
| | crossarch_equivalence — same function across two ISAs? (+distractors) | medium | deterministic stance+symbol | 25 | | |
| | agentic_flag_hunt — find argv[1] that makes a shipped binary print OK (sandbox-executable) | practice | exact flag / program stdout + exit-code probe | 29 | | |
| | library_ident — SBOM-style: name+version of a production library from artifacts | easy/med | exact substring (name+version) | 14 | | |
| | arch_semantics_qa / isa_semantics_qa — instruction-sequence semantics | medium | rubric judge | 14 | | |
| | pe_import_reasoning — infer program behavior from imports+strings | medium | rubric judge | 4 | | |
| | pe_structure_qa — PE header/section facts | easy | rubric judge | 3 | | |
| Architectures: x86-64 (Linux ELF, Windows PE), i686, AArch64, ARM32, RISC-V, MIPS(o32/n64), PPC64LE. | |
| Sources: Ubuntu production binaries (649 distinct third-party file names referenced in shipped provenance; | |
| system libs incl. libcrypto, zstd, | |
| sqlite3, libxml2, zlib, libpng, plus Mesa DRI drivers, QEMU device models, systemd internals, | |
| Wireshark/PulseAudio/gprofng/rsyslog plugins), **Debian production cross-toolchain glibc | |
| (AArch64/RISC-V/MIPS64/PPC64LE)**, **Android/Termux production aarch64 libs (libsqlite3 3.54.1, | |
| libpng 1.6.59, libarchive 3.8.10, libssh 0.12.2, libcurl 8.23.0, OpenSSL 3.6.5, ncurses 6.5, | |
| expat 2.9.0 — platform-tagged in provenance)**, curated PE samples, cross-compiled targets | |
| (honestly labeled). | |
| ## Automated re-verification through our in-house analysis pipeline (same-day, sampling + full-set) | |
| - **9,865/9,865** checkable items: answer FUNC verified at exact address & size via readelf. | |
| - **318/318** sampled disassembly excerpts byte-exact vs objdump; zero unscrubbed names. | |
| - MC decoy rules hold on all MC items (verified at audit; hardening only removed items, never added); exactly-one-correct-option verified on all (1 defect found & removed). | |
| - Post-audit hardening: **238 items given full alias acceptance** (multiple valid FUNC names at one address), **130 same-size twin items filtered out** (answer not uniquely inferable), 18 unresolvable-provenance items dropped. The audit's findings were fixed, not hidden. | |
| ## Construction & verification | |
| - Produced by static analysis through our in-house analysis pipeline (Ghidra, binutils objdump/readelf/nm). No target binaries executed for answers. | |
| - Scrubbing: every symbol equal to the answer or a callee replaced with `<...>`; automated answer-leak scan through our in-house analysis pipeline: **0/10,251 hits**. | |
| - Ground truth derived from artifact itself (symbol tables, header fields, decompiler output). | |
| - Auto-grader included (`grading.py`): deterministic graders for exact/MC/crossarch/flag/library families (exact-match is alias-tolerant); rubric judge for open-ended. Test harness `test_grading.py`: 10,172 deterministic items scored 1.0 on gold answers, 0.0 on abstention; 79 routed to judge. | |
| - **Full agentic benchmark measured** (`EVAL_RESULTS-passk.md`, 2026-10-29): Claude Opus 5.5 solved **29/29 envs, 145/145 attempts** with tools (median 44 s) — every answer byte-exact vs our gold: oracle confirmation that the shipped binary accepts those answers and verifies our grading chain; not a proof that each answer is unique. The public tier is practice/warm-up level for tool-using frontier models. Private task families are calibrated across several models on fresh, never-published instances; results are shared privately with prospective buyers. A pilot can include buyer-run per-model pass-rate calibration with our harness, graders, and analysis report. Single-shot no-tools-requested ID reference stays 7/12 small-n, and a fixed 20-item agentic hard spot check found the sampled command-output/static-analysis families practice-level for Opus with tools (40/40 attempts; non-isolated host run, upper bound; `EVAL_RESULTS-frontier.md` §C). | |
| - Agentic challenges: round 1 (8) + round 2 (6, stripped) + **round 3 (6: self-decrypting .text via .init_array, ptrace/TracerPid/timing anti-instrumentation, chained-seed stages, masked-blob hash + explicit_bzero, control-flow flattening with decoy states, encrypted-bytecode VM with permuted opcodes)** + **round 4 (5: two-stage ARX+FNV memory-only chain, MIPS-BE branch-delay-slot trap, Windows PE TLS-callback packer, AArch64 computed-`blr` chain with PAC noise, RISC-V PC-relative jump-table `jalr`)** + **round 5 (4: x86-64 static-pie relocation-table/GOT-indirect hunt, ARM32 Thumb/ARM interworking trap, PPC64LE condition-register juggling gate, PE PEB-walk API hiding with export-name hashing)** — the set covers multiple distinct RE techniques; every binary verified OK-on-flag / NO-on-mutation (24 single-char mutations each, zero false OKs); exit status = first failing position enables black-box probing; `strings` shows no flag fragments. | |
| - Provenance per item: binary name and sha256_16 on every item; function address/size and architecture where applicable. | |
| ## Intended use | |
| RL training (GRPO-style verifiable rewards), post-training eval suites, expert-model benchmarks, | |
| SFT for code-understanding models. Runnable as-is on Prime Intellect Environments Hub via the | |
| bundled `verifiers` v1 taskset (`environment/`), or standalone JSONL. | |
| Harbor-native: the 29 agentic envs are Harbor task folders (verified: each has task.toml + | |
| instruction.md + Dockerfile + tests/test.sh + solution scripts and solution explanations) — runnable with `harbor run`, and importable **for free** | |
| into HUD ("HUD reads Harbor task folders as they are", docs.hud.ai/platform/harbor-import). Pilot zip: | |
| https://huggingface.co/datasets/S3RL4/binary-reasoning-v1/resolve/main/harbor_pilot_29.zip | |
| ## Positioning vs existing work (surveyed 2026-10-28) | |
| | neighbor | what it is | how binary-reasoning-v1 differs | | |
| |---|---|---| | |
| | CrackMeBench (arXiv:2605.10597, 5/2026) | educational binary-reasoning agent tasks with executable oracles | validates the genre; our set uses production binaries (Ubuntu/Debian glibc/Termux-Android), 10,251 items, and 10 families including cross-arch and SBOM-style identification | | |
| | knewstimek/crackme-agent-bench (GitHub) | small Windows PE reverse-engineering benchmark with MCP debugger tools; **analyzed statically through our in-house analysis pipeline**: v1 fully static-readable; v2/v3 = TLS-callback packer, `.text` encrypted at rest, API hidden via PEB-walk, memory-only flag (hash of encoded blob, wiped) | different scope: no input channel or oracle; ours adds argv + exit-status probing. Their packing/anti-debug techniques inform our roadmap below | | |
| | CyberGym (sunblaze-ucb, HF dl≈369k) | crash/vuln-finding RL at scale | demand proof for binary-security data; different task (dynamic crashes vs static RE reasoning) | | |
| | cybench-harbor (HF dl≈1.5k) | Cybench CTFs in Harbor format | precedent that security tasks ship via Harbor — our 29 agentic tasks use the same packaging | | |
| | AssemblyBench (arXiv:2604.03750 / HF) | **robotics** mechanical assembly — name collision only | unrelated | | |
| | atul10 RE code datasets, ollvm-decompilation (HF dl<400) | source-level or decompiler-output pairs | no executable oracles, no verification bar, single arch | | |
| Conclusion: our niche is verified, scrubbed, oracle-graded production-binary RE plus the agentic env set, not "no such data exists anywhere." Academic function-name-prediction datasets exist at scale (e.g. AGNOMIN arXiv:2509.25514: 396k functions / 3 architectures); our differentiator is verification, provenance, deterministic grading, and the agentic environments. | |
| Extension capability demonstrated across two rounds: 9 extension envs built+container-verified spanning x86-64 | |
| (chain+FNV; static-pie relocation-table hunt), MIPS BE (delay slots), AArch64 (PAC + computed blr), RISC-V | |
| (PC-relative jump table), ARM32 (Thumb/ARM interworking), PPC64LE (condition-register gate) and Windows PE | |
| (TLS-callback packing; PEB-walk API hiding) — `harbor_round4/` + `harbor_round5/`, per-env evidence dossiers, | |
| demo zips on the HF repo; 31 more designs specified in AGENTIC-ROADMAP.md. | |
| ## Known limits | |
| - 79 items route to an LLM judge (53 func_summary, 14 arch/isa-semantics QA, 7 PE reasoning, 5 inferred-stripped func_id); the other 10,172 grade deterministically. Judge model configurable. | |
| - Some stripped-binary answers are marked `inferred:true`; graders accept role-correct names. | |
| - Address coordinate systems vary (PIE load base noted in provenance where relevant). | |
| ## Files | |
| - `environment/` — verifiers v1 package (taskset.py, grading.py, items.jsonl, bins/ + src/, README.md, pyproject.toml); **import-tested against real verifiers v1: all 10,251 tasks load from the full-pack copy inside `binary-reasoning-v1.zip`; the loose working-tree `environment/src/binary_reasoning_v1/items.jsonl` is the 100-item open sample** | |
| - `verification_kit/` — sample-level public checks (`gold.py`, `do_nothing.py`, `random_wrong.py`, `leak_check.py`, `grader_tamper.py`) plus internal hard-family reproduction scripts under `solvers/`; see `verification_kit/README.md` for limitations. | |
| - `VERIFICATION-DOSSIER-PUBLIC.md` — public summary of verified proof and known caveats; full buyer-ready dossier is private until published. | |
| - `harbor/` — the 29 agentic challenges are packaged in **Harbor task format** (harbor/ + harbor_round4/ + harbor_round5/) (instruction.md, task.toml, Dockerfile, `solution/solve.sh`, `solution/EXPLANATION.md`, tests/test.sh → reward.txt); **built and scored in real Docker containers: 29/29 reference solve→reward 1; wrong, tampered-binary, and hard-mode controls →reward 0** (`verify_pilot29_docker.sh`; `RESULT envs_ok=29 envs_bad=0`) | |
| - `part_relf/repe/xarch/bulk/agentic/agentic2/isa/glibc/android.jsonl` — source batches | |
| - `harbor_round4/`+`part_agentic4.jsonl`+`round4/src`, `harbor_round5/`+`part_agentic5.jsonl`+`round5/src` — rounds 4–5 proof envs (9 total: x86-64×2/MIPS/AArch64/RISC-V/ARM32/PPC64LE/PE×2), **merged into the canonical pack 2026-10-29** (count 10,251, docs re-cut); per-env evidence: `harbor_round4/EVIDENCE.md`, `round5/EVIDENCE-round5.md` | |
| - `sample_free.jsonl` — 9-item free sample (one per family) for listings | |
| - `test_grading.py`, `leak_audit.py` — verification harnesses | |