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baradok60915d52e8425 f58eafeb48 autoreg-skills-max: initial public release (restored, redacted, hardened)

2026-10-20 02:16:32 +03:00

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LLM Vision Captcha Solver

Use multiple vision-capable LLMs in parallel to vote on visual captcha solutions — bypasses Arkose FunCaptcha, hCaptcha, and similar visual challenges without paid captcha-solving services.

When to Use

Arkose FunCaptcha on GitHub signup, xAI, or any protected page
Paid captcha services (CapSolver, YesCaptcha, 2captcha) are unavailable or too slow
You have vision LLM API access (Aurora gateway, OpenRouter, direct provider keys)
The captcha is image-based (not audio/text)
Self-hosted turnkey solver wanted instead of custom voting code → references/self-hosted-solver-and-free-quota-2026-09-30.md (waguriagentic/captcha-solver: 11 types incl. arkose/datadome/turnstile, free Mistral vision backend, verified deployment recipe + free-quota API catalog)
hCaptcha on a live registration farm → same reference file, section "hCaptcha battle-tested": real_page:true payload contract, force-click checkbox cascade, rotation-task prompt, stale-sidecar/port traps, paid-solver pre-audit, three-layer vision-model config (mistral default + solve.py env + watchdog extra_env), timeout-chain ordering (client > payload > server), and empty-task-text diagnosis. The free sidecar solves plain grid hCaptcha, but rotation/drag variants yield ≈0 even when the checkbox opens, the challenge frame loads and grid cells get picked — hCaptcha silently rejects the answers and the token comes back empty after the full timeout. Audit the ACTUAL challenge type on the target before promising a reg throughput; on rotation/drag the free path is not a substitute for a paid solver.

Core Pattern: Multi-Model Voting

1. Screenshot captcha challenge
2. Local PIL image enhancement (Lanczos upscale + sharpen + contrast)
3. Detect puzzle variant from challenge text
4. Screenshot all candidate answers → stitch into numbered grid
5. Parallel query to 3-4 vision models with identical prompt
6. Parse ANSWER=N from each response
7. Majority vote (tiebreak: model priority order)
8. Auto-click winning answer
9. Save annotated review image for debugging

Model Pool (by accuracy on captcha tasks)

Priority	Model	Notes
1	gemini-3.5-flash	Most accurate on orientation/matching
2	gpt-5.5	Strong all-rounder
3	gemini-3.1-pro	Good on sequences
4	claude-opus-4.8	Weakest on these images, lowest weight

All models run in parallel via ThreadPoolExecutor with a 55s deadline (Arkose timeout ~60s).

Implementation Essentials

1. Image Enhancement (before sending to LLM)

Critical: compressed JPEG with size limits. PNG grids of 7 stitched 3D images hit 13MB — HTTP timeout guaranteed.

from PIL import Image, ImageEnhance, ImageFilter

im = Image.open(path).convert("RGB")
im = im.resize((im.width * 2, im.height * 2), Image.LANCZOS)
r = 1600 / max(im.width, im.height) if max(im.width, im.height) > 1600 else 1
if r < 1: im = im.resize((int(im.width*r), int(im.height*r)), Image.LANCZOS)
im = im.filter(ImageFilter.MedianFilter(size=3))
im = ImageEnhance.Contrast(im).enhance(1.6)
im = ImageEnhance.Brightness(im).enhance(1.15)
im = ImageEnhance.Sharpness(im).enhance(2.2)
im = im.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=2))
im.save(path, "JPEG", quality=82)

2. Candidate Grid Stitching

Stitch N candidate images into one labeled grid with numbered cells. Add reference image at top if available. LLM compares all options in one call.

3. Parallel Query with Refusal Detection

_REFUSAL_MARKERS = (
    "cannot fulfill", "can't fulfill", "cannot assist",
    "i am unable", "i'm unable", "safety guidelines",
    "not able to help", "cannot help with that",
)

def _looks_like_refusal(text):
    if not text: return True
    return any(m in text.lower() for m in _REFUSAL_MARKERS)

with ThreadPoolExecutor(max_workers=4) as ex:
    futs = {ex.submit(query_model, args): name for args in VOTER_MODELS}
    for fut in as_completed(futs, timeout=55):
        txt = fut.result()
        if not _looks_like_refusal(txt):
            a = parse_answer(txt)  # regex: ANSWER\s*=\s*(\d+)
            answers.append(a)

4. Gateway Flexibility

Support both OpenAI-compatible (/v1/chat/completions) and Anthropic-native (/v1/messages) APIs. Detect by URL pattern.

5. Review Annotation

Draw colored boxes on grid image: RED = final vote, colored per-model = individual votes, footer = model→answer legend. Saves to screenshots/REVIEW_rN.png.

Captcha Variant Detection

Parse challenge text for puzzle type:

sequence: 4 icons in sequential rings → vote per ring
rotate: 3D orientation matching → vote rotation
character: figure on grid → HARDEST, consider skip
wires: connect wires to icons → vote pairing

Aurora Gateway Integration

VISION_API_BASE=http://127.0.0.1:8080
VISION_API_KEY=enigothbreach2026master
VOTE_ZZ_BASE=http://127.0.0.1:8080
VOTE_ZZ_KEY=enigothbreach2026master
VOTE_GPT_KEY=enigothbreach2026master

Aurora (557 models, 111 providers) routes to live dashscope/google/groq keys.

VALIDATED 2026-09-30 — repo module works on Aurora unmodified: point the env vars above at the gateway, override AC.VOTER_MODELS with (base, key, model_id) tuples, and vote_answer() returns a correct majority in ~7s. Verified: gemini-3.6-flash ✅, gemini-3.1-pro ✅, qwen-vl-max ✅ (all 3 voted correctly on a 4-tile match fixture); claude-sonnet-4-6 returned empty content via Aurora (refusal filter drops it cleanly — 3-model quorum still wins). Function map: ask_vision, vote_answer, vote_score, stitch_options_grid, enhance_local, parse_json_answer, _looks_like_refusal, gh_pick_prompt.

Aurora startup pitfall: ./aurora.exe --config configs/config.yaml exits immediately (prints help and dies). Working start: set -a; source .env; set +a; AURORA_CONFIG_PATH=C:/Users/User/aurora-gateway/configs/config.yaml ./aurora.exe in background, then poll /v1/models up to 25s (startup is slow).

Run the verification script any time the stack changes: scripts/test_agent_captcha_aurora.py (local function tests + live voting round, needs repo clone at REPO_PATH or default C:/Users/User/tmp/reg-factory-github). Test detail: references/voting-test-aurora-2026-09-30.md.

Pitfalls

Payload size: PNG grids hit 13MB → timeout. JPEG compress with max_side=1600.
wag-solver apikey.txt: self-hosted waguriagentic/captcha-solver 500s (Errno 2) until common/apikey.txt exists; first solve blocks ~15min on a 535MB cloakbrowser Chromium download — background + poll the process log, never kill mid-download.
Model refusal: Claude refuses captcha tasks. Filter via _REFUSAL_MARKERS.
Deadline race: Arkose ~60s timeout. Voting deadline 55s max.
GitHub blob req: GitHub Arkose needs data-exchange blob from #funcaptcha[data-data-exchange-payload].
Character variant: Lowest success rate. Skip and retry with new window.
Slow models: Per-model timeout 35s.
Paid-solver pre-audit must be createTask, not balance: a positive balance in a scan can coexist with ERROR_KEY_DOES_NOT_EXIST on createTask (banned/orphaned account) — the key is unusable regardless of the shown funds. Also verify the provider still supports the exact task type: CapMonster dropped hCaptcha (ERROR_TASK_NOT_SUPPORTED on both proxyless and proxied variants) while still accepting recaptcha/turnstile/funcaptcha. One live createTask with the real sitekey is the only valid proof before funding or integrating a solver.

Source

Full impl: https://github.com/GoubaLab/reg-factory-github/blob/main/common/agent_captcha.py (809 lines)

bypass-ai — captcha bypass via paid services
anti-detection-browser — browser fingerprint evasion
fork-analyzer — repo audit workflow

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