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Security audit

Signal Verification Network: Cryptographic Proof for Trading Signals

Security checks for vulnerabilities and agentic risk

Overview

This is a non-executable guide, but it gives production-framed examples for automated escrow payments and disputes with under-scoped verification and credential handling risks.

Review this carefully before installing or following it. Treat the code as illustrative only: do not run the escrow release, dispute, webhook, or scheduled monitor examples against real funds without adding human approval, server-side verification of immutable records, strict key management, pinned dependencies, and tests for failed verification cases.

A.I.G

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
Findings (3)

T09 · Insecure Skill Coding Practices

Error
Location
SKILL.md:1208
Finding

Caller-Controlled Verification State Can Authorize Escrow Release

Content
View full analysis
None: """Record a verified signal outcome.""" if not outcome.verified: raise ValueError( f"Signal {outcome.signal_id} is not verified. " "Only verified signals can be recorded." ) self._outcomes.append(outcome) ``` ```python def evaluate_subscription(client: SimpleClient, verifier: SignalVerifier, tracker: PerformanceTracker, escrow_id: str, provider_id: str, criteria: dict) -> dict: """Evaluate whether a subscription's performance criteria are met.""" # Compute current stats from verified data stats = tracker.compute_stats() checks = { "hit_rate": stats["hit_rate"] >= criteria.get("min_hit_rate", 0), "risk_reward": ( stats["risk_reward_ratio"] >= criteria.get("min_risk_reward", 0) ), "drawdown": ( stats["max_consecutive_losses"] <= criteria.get("max_drawdown", float("inf")) ), "signal_count": ( stats["total_signals"] >= criteria.get("min_signal_count", 0) ), } all_passed = all(checks.values()) if all_passed: release_result = client._execute("release_escrow", { "escrow_id": escrow_id, }) return { "status": "passed", "checks": checks, "stats": stats, "action": "escrow_released", "release_result": release_result, } ``` ### Technical Analysis The guide represents outcome verification with the mutable `SignalOutcome.verified` Boolean. An ...[truncated 2176 chars]
Remediation
View remediation

T09 · Insecure Skill Coding Practices

Error
Location
SKILL.md:1261
Finding

Escrow Drawdown Criterion Is Evaluated Using an Unrelated Metric

Content
View full analysis
Remediation
View remediation

T08 · Insecure Dependencies

Warning
Location
SKILL.md:130
Finding

Security-Sensitive Dependencies Are Installed Without Version or Integrity Pinning

Content
View full analysis
Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (17)

Intent-Code Divergence

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The documentation says dispute evidence is submitted with the dispute, but the implementation only sends escrow_id and reason, silently discarding the assembled evidence package. In a system centered on verifiable disputes, this can cause failed or weak dispute submissions, improper escrow outcomes, and loss of subscriber funds because the adjudicator never receives the cryptographic proof the workflow depends on.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The skill requires an AGENT_SIGNING_KEY and includes examples passing private key material directly into application code, but it does not clearly warn about secure generation, storage, rotation, non-logging, or separation between sandbox and production keys. Mishandling this credential would let an attacker forge signal commitments, reveals, and outcomes as the provider, undermining the entire trust model.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · SKILL.md (reported line 33)May include surrounding context.

md
> - `AGENT_SIGNING_KEY`: Cryptographic signing key for agent identity (Ed25519 key pair for request signing)


Signal providers charge $39-400 per month with zero accountability. No proof that signals were issued before price moves. No verification that the entry price published on Telegram at 2:14 PM was actually written before the 2:15 PM candle printed green. Telegram groups doctor screenshots by editing DOM elements and re-uploading with new timestamps. Discord bots synthesize equity curves from cherry-picked trades. Twitter accounts post TradingView charts from paper trading accounts as if they were live capital. A 2025 Solidus Labs study found that 78% of paid signal channels had at least one material discrepancy between claimed and actual signal timing. The $2.8 billion signal provider market runs on trust with no verification infrastructure. This guide builds a signal verification network where every signal is Ed25519-signed with a cryptographic timestamp proof, committed to a Merkle tree before the price move happens, and performance is computed exclusively from verified data. Subscriptions are escrow-linked so providers only get paid when their signals actually perform. Every pattern runs on the GreenHelix A2A Commerce Gateway API. Every code example is production-ready. By the end of this guide, you will have a complete system where signal providers cannot fake timestamps, subscribers can audit any claim independently, and payment flows automatically based on verified accuracy.
1. [The Signal Trust Crisis](#chapter-1-the-signal-trust-crisis)
2. [The SignalPublisher Class](#chapter-2-the-signalpublisher-class)

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · SKILL.md (reported line 52)May include surrounding context.

md
> - `AGENT_SIGNING_KEY`: Cryptographic signing key for agent identity (Ed25519 key pair for request signing)


Signal providers charge $39-400 per month with zero accountability. No proof that signals were issued before price moves. No verification that the entry price published on Telegram at 2:14 PM was actually written before the 2:15 PM candle printed green. Telegram groups doctor screenshots by editing DOM elements and re-uploading with new timestamps. Discord bots synthesize equity curves from cherry-picked trades. Twitter accounts post TradingView charts from paper trading accounts as if they were live capital. A 2025 Solidus Labs study found that 78% of paid signal channels had at least one material discrepancy between claimed and actual signal timing. The $2.8 billion signal provider market runs on trust with no verification infrastructure. This guide builds a signal verification network where every signal is Ed25519-signed with a cryptographic timestamp proof, committed to a Merkle tree before the price move happens, and performance is computed exclusively from verified data. Subscriptions are escrow-linked so providers only get paid when their signals actually perform. Every pattern runs on the GreenHelix A2A Commerce Gateway API. Every code example is production-ready. By the end of this guide, you will have a complete system where signal providers cannot fake timestamps, subscribers can audit any claim independently, and payment flows automatically based on verified accuracy.
1. [The Signal Trust Crisis](#chapter-1-the-signal-trust-crisis)
2. [The SignalPublisher Class](#chapter-2-the-signalpublisher-class)

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · SKILL.md (reported line 33)May include surrounding context.

md
> - `AGENT_SIGNING_KEY`: Cryptographic signing key for agent identity (Ed25519 key pair for request signing)


Signal providers charge $39-400 per month with zero accountability. No proof that signals were issued before price moves. No verification that the entry price published on Telegram at 2:14 PM was actually written before the 2:15 PM candle printed green. Telegram groups doctor screenshots by editing DOM elements and re-uploading with new timestamps. Discord bots synthesize equity curves from cherry-picked trades. Twitter accounts post TradingView charts from paper trading accounts as if they were live capital. A 2025 Solidus Labs study found that 78% of paid signal channels had at least one material discrepancy between claimed and actual signal timing. The $2.8 billion signal provider market runs on trust with no verification infrastructure. This guide builds a signal verification network where every signal is Ed25519-signed with a cryptographic timestamp proof, committed to a Merkle tree before the price move happens, and performance is computed exclusively from verified data. Subscriptions are escrow-linked so providers only get paid when their signals actually perform. Every pattern runs on the GreenHelix A2A Commerce Gateway API. Every code example is production-ready. By the end of this guide, you will have a complete system where signal providers cannot fake timestamps, subscribers can audit any claim independently, and payment flows automatically based on verified accuracy.
1. [The Signal Trust Crisis](#chapter-1-the-signal-trust-crisis)
2. [The SignalPublisher Class](#chapter-2-the-signalpublisher-class)

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · SKILL.md (reported line 52)May include surrounding context.

md
> - `AGENT_SIGNING_KEY`: Cryptographic signing key for agent identity (Ed25519 key pair for request signing)


Signal providers charge $39-400 per month with zero accountability. No proof that signals were issued before price moves. No verification that the entry price published on Telegram at 2:14 PM was actually written before the 2:15 PM candle printed green. Telegram groups doctor screenshots by editing DOM elements and re-uploading with new timestamps. Discord bots synthesize equity curves from cherry-picked trades. Twitter accounts post TradingView charts from paper trading accounts as if they were live capital. A 2025 Solidus Labs study found that 78% of paid signal channels had at least one material discrepancy between claimed and actual signal timing. The $2.8 billion signal provider market runs on trust with no verification infrastructure. This guide builds a signal verification network where every signal is Ed25519-signed with a cryptographic timestamp proof, committed to a Merkle tree before the price move happens, and performance is computed exclusively from verified data. Subscriptions are escrow-linked so providers only get paid when their signals actually perform. Every pattern runs on the GreenHelix A2A Commerce Gateway API. Every code example is production-ready. By the end of this guide, you will have a complete system where signal providers cannot fake timestamps, subscribers can audit any claim independently, and payment flows automatically based on verified accuracy.
1. [The Signal Trust Crisis](#chapter-1-the-signal-trust-crisis)
2. [The SignalPublisher Class](#chapter-2-the-signalpublisher-class)

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The guide simultaneously labels itself educational and then claims every code example is "production-ready," even though the code uses insecure shortcuts such as in-memory pending commit storage and placeholder operational assumptions. This mismatch can cause operators to deploy examples without adding persistence, key-management hardening, retries, validation, or audit controls, leading to integrity and availability failures in a financial workflow.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 152)May include surrounding context.

md
"""Publishes cryptographically signed trading signals with commit-reveal."""

    def __init__(self, agent_id: str, api_key: str, private_key_b64: str,
                 base_url: str = "https://api.greenhelix.net/v1"):
        self.agent_id = agent_id
        self.api_key = api_key
        self.base_url = base_url

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 572)May include surrounding context.

md
"""Publishes cryptographically signed trading signals with commit-reveal."""

    def __init__(self, agent_id: str, api_key: str, private_key_b64: str,
                 base_url: str = "https://api.greenhelix.net/v1"):
        self.agent_id = agent_id
        self.api_key = api_key
        self.base_url = base_url

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 1304)May include surrounding context.

md
"""Publishes cryptographically signed trading signals with commit-reveal."""

    def __init__(self, agent_id: str, api_key: str, private_key_b64: str,
                 base_url: str = "https://api.greenhelix.net/v1"):
        self.agent_id = agent_id
        self.api_key = api_key
        self.base_url = base_url

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 2143)May include surrounding context.

md
"""Publishes cryptographically signed trading signals with commit-reveal."""

    def __init__(self, agent_id: str, api_key: str, private_key_b64: str,
                 base_url: str = "https://api.greenhelix.net/v1"):
        self.agent_id = agent_id
        self.api_key = api_key
        self.base_url = base_url

External Transmission

Medium
Category
Data Exfiltration
Confidence
70% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 171)May include surrounding context.

md
def _execute(self, tool: str, input_data: dict) -> dict:
        """Execute a tool on the GreenHelix gateway."""
        response = requests.post(
            f"{self.base_url}/v1",
            headers=self.headers,
            json={"tool": tool, "input": input_data},

External Transmission

Medium
Category
Data Exfiltration
Confidence
70% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 584)May include surrounding context.

md
def _execute(self, tool: str, input_data: dict) -> dict:
        """Execute a tool on the GreenHelix gateway."""
        response = requests.post(
            f"{self.base_url}/v1",
            headers=self.headers,
            json={"tool": tool, "input": input_data},

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 421)May include surrounding context.

md
COMMITMENT_HASH="a1b2c3d4e5f6..."  # SHA-256 of canonical signal JSON
SIGNATURE_B64="base64-encoded-signature"

curl -s -X POST https://sandbox.greenhelix.net/v1 \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · SKILL.md (reported line 553)May include surrounding context.

md
### Why Buyers Need Independent Verification

A signal provider claims 72% accuracy over 90 days. The provider's website shows a beautiful equity curve and a table of winning trades. Without verification, you are trusting the provider to honestly report their own performance -- the same trust model that has failed for every Telegram signal group, every copy trading platform, and every signal marketplace built to date. The SignalVerifier class lets a subscriber independently verify every signal against the cryptographic record on GreenHelix. No trust required.

### The Complete SignalVerifier Class

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 1128)May include surrounding context.

With curl:

bash
curl -s -X POST https://sandbox.greenhelix.net/v1 \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{

External Transmission

Medium
Category
Data Exfiltration
Confidence
70% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 1314)May include surrounding context.

md
class SimpleClient:
    def _execute(self, tool, input_data):
        resp = requests.post(
            f"{BASE_URL}/v1",
            headers=headers,
            json={"tool": tool, "input": input_data},

Static analysis

No suspicious patterns detected.