AIQuant aiquant.tools

AIQuant concentrates algorithmic trading bots, ML-driven signal generation, exchange API key management, backtesting infrastructure, and real-time portfolio optimization — fertile ground for API key theft, model manipulation, unauthorized trade execution, and cross-account data leaks. This playbook rewards only highest-impact, fully demonstrated criticals — each earns $5,000.

AIQuant's AI models make trading decisions based on market data and user configurations. Compromising the model training pipeline, signal distribution, or trade execution engine can cascade into large-scale fund movement across connected exchanges. In scope materially: every host under aiquant.tools, subdomains, AI/ML backend services, bot engine APIs, websocket data feeds, admin panels, database surfaces, and user API key storage. Civil exclusions only: fund theft against unrelated third parties, laundering, harassment — society-level bans, not endpoint lists.
Always keep sandbox mirrors humane: prove impact on isolated test accounts when possible.

A critical finding is one that leads to database compromise, admin account or console access, drainage of user funds (via stolen API keys, trade execution without consent, or model/signal manipulation), or any vulnerability enabling fund theft from the platform or its users.

• SQL injection, NoSQL injection, or any DB access path that exfiltrates credentials, API keys, or user data
• Authentication bypass granting admin-level access to the aiquant.tools control panel
• Server-side request forgery (SSRF) exposing internal infrastructure, secrets, or database endpoints
• Remote code execution (RCE) on aiquant.tools servers
• IDOR or privilege escalation allowing access to other users' API keys, bot configurations, or trading history
• Model poisoning or signal injection that causes automated fund drainage

Signal generation workers, bot execution daemons, ML model training infrastructure, monitoring dashboards, staging shards on aiquant.tools — all eligible when critical impact reproduces.

• Vanity domains CNAME'd into AIQuant edge — show DNS/traffic proof in your write-up.
• AI model API endpoints that serve trading signals to bots
• Colocated automation containers AIQuant operates
• Staging or dev shards on aiquant.tools that mirror production

1. Prove exploitation success. Provide scripts, HARs, parallel request captures, or signing traces showing fund/state impact — not paper analysis alone.
2. Number your hops. Session/API primitive → escalation → treasury or book integrity break.
3. Minimize real-user blast radius. Prefer test accounts, low-value targets, and reversible payloads when demonstrating theft-class bugs.
4. Private coordination first. Use official intake on aiquant.tools before public threads.
5. Duplicate fairness. First qualifying PoC wins; followers close acknowledged-only.

Use the security / vulnerability disclosure contact published on https://aiquant.tools/ (security@aiquant.tools is a common guess — verify on the live site before emailing). Attach a clear kill-chain narrative along with replayable proof.

Unverifiable reports or those lacking demonstration of actual impact will be closed without reward.

# BUGGY: API key endpoint returns full key in response without authorization check
@app.route('/api/v1/user/apikeys')
def get_api_keys():
    user_id = request.args.get('user_id')
    # No ownership check — returns keys for any user_id
    keys = db.query("SELECT * FROM api_keys WHERE user_id = ?", (user_id,))
    return jsonify(keys)

A real submission would pair this with captured HTTP requests showing API key exfiltration for arbitrary users on aiquant.tools, then demonstrate using those keys to execute unauthorized trades on connected exchanges.