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Substrate Vulnerability Scanner

OrganizationPopular
trailofbits
substrate-vulnerability-scanner

Scans Substrate/Polkadot pallets for 7 critical vulnerabilities including arithmetic overflow, panic DoS, incorrect weights, and bad origin checks. Use when auditing Substrate runtimes or FRAME pallets.

Overview

Publishertrailofbits
Repositoryskills
Skill namesubstrate-vulnerability-scanner
Stars
7.1K
Forks
611
Bundled files
3
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Substrate Vulnerability Scanner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/building-secure-contracts/skills/substrate-vulnerability-scanner .claude/skills/substrate-vulnerability-scanner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Substrate Vulnerability Scanner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Substrate Vulnerability Scanner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Substrate Vulnerability Scanner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Substrate Vulnerability Scanner

1. Purpose

Systematically scan Substrate runtime modules (pallets) for platform-specific security vulnerabilities that can cause node crashes, DoS attacks, or unauthorized access. This skill encodes 7 critical vulnerability patterns unique to Substrate/FRAME-based chains.

2. When to Use This Skill

  • Auditing custom Substrate pallets
  • Reviewing FRAME runtime code
  • Pre-launch security assessment of Substrate chains (Polkadot parachains, standalone chains)
  • Validating dispatchable extrinsic functions
  • Reviewing weight calculation functions
  • Assessing unsigned transaction validation logic

3. Platform Detection

File Extensions & Indicators

  • Rust files: .rs

Language/Framework Markers

rust
// Substrate/FRAME indicators
#[pallet]
pub mod pallet {
    use frame_support::pallet_prelude::*;
    use frame_system::pallet_prelude::*;

    #[pallet::config]
    pub trait Config: frame_system::Config { }

    #[pallet::call]
    impl<T: Config> Pallet<T> {
        #[pallet::weight(10_000)]
        pub fn example_function(origin: OriginFor<T>) -> DispatchResult { }
    }
}

// Common patterns
DispatchResult, DispatchError
ensure!, ensure_signed, ensure_root
StorageValue, StorageMap, StorageDoubleMap
#[pallet::storage]
#[pallet::call]
#[pallet::weight]
#[pallet::validate_unsigned]

Project Structure

  • pallets/*/lib.rs - Pallet implementations
  • runtime/lib.rs - Runtime configuration
  • benchmarking.rs - Weight benchmarks
  • Cargo.toml with frame-* dependencies

Tool Support

  • cargo-fuzz: Fuzz testing for Rust
  • test-fuzz: Property-based testing framework
  • benchmarking framework: Built-in weight calculation
  • try-runtime: Runtime migration testing

4. How This Skill Works

When invoked, I will:

  1. Search your codebase for Substrate pallets
  2. Analyze each pallet for the 7 vulnerability patterns
  3. Report findings with file references and severity, above them a coverage table carrying a verdict for every pattern
  4. Provide fixes for each identified issue
  5. Check weight calculations and origin validation

5. Vulnerability Patterns (7 Critical Patterns)

I check for 7 critical vulnerability patterns unique to Substrate/FRAME. For detailed detection patterns, code examples, mitigations, and testing strategies, see VULNERABILITY_PATTERNS.md.

Pattern Summary:

  1. Arithmetic Overflow ⚠️ CRITICAL

    • Direct +, -, *, / operators wrap in release mode
    • Must use checked_* or saturating_* methods
    • Affects balance/token calculations, reward/fee math
  2. Don't Panic ⚠️ CRITICAL - DoS

    • Panics cause node to stop processing blocks
    • No unwrap(), expect(), array indexing without bounds check
    • All user input must be validated with ensure!
  3. Weights and Fees ⚠️ CRITICAL - DoS

    • Incorrect weights allow spam attacks
    • Fixed weights for variable-cost operations enable DoS
    • Must use benchmarking framework, bound all input parameters
  4. Verify First, Write Last ⚠️ HIGH (Pre-v0.9.25)

    • Storage writes before validation persist on error (pre-v0.9.25)
    • Pattern: validate → write → emit event
    • Upgrade to v0.9.25+ or use manual #[transactional]
  5. Unsigned Transaction Validation ⚠️ HIGH

    • Insufficient validation allows spam/replay attacks
    • Prefer signed transactions
    • If unsigned: validate parameters, replay protection, authenticate source
  6. Bad Randomness ⚠️ MEDIUM

    • pallet_randomness_collective_flip vulnerable to collusion
    • Must use BABE randomness (pallet_babe::RandomnessFromOneEpochAgo)
    • Use random(subject) not random_seed()
  7. Bad Origin ⚠️ CRITICAL

    • ensure_signed allows any user for privileged operations
    • Must use ensure_root or custom origins (ForceOrigin, AdminOrigin)
    • Origin types must be properly configured in runtime

For complete vulnerability patterns with code examples, see VULNERABILITY_PATTERNS.md.


6. Scanning Workflow

Step 1: Platform Identification

  1. Verify Substrate/FRAME framework usage
  2. Check Substrate version (v0.9.25+ has transactional storage)
  3. Locate pallet implementations (pallets/*/lib.rs)
  4. Identify runtime configuration (runtime/lib.rs)

Step 2: Dispatchable Analysis

For each #[pallet::call] function:

  • Arithmetic: Uses checked/saturating operations?
  • Panics: No unwrap/expect/indexing?
  • Weights: Proportional to cost, bounded inputs?
  • Origin: Appropriate validation level?
  • Validation: All checks before storage writes?

Step 3: Panic Sweep

bash
# Search for panic-prone patterns
rg "unwrap\(\)" pallets/
rg "expect\(" pallets/
rg "\[.*\]" pallets/  # Array indexing
rg " as u\d+" pallets/  # Type casts
rg "\.unwrap_or" pallets/

Step 4: Arithmetic Safety Check

bash
# Find direct arithmetic
rg " \+ |\+=| - |-=| \* |\*=| / |/=" pallets/

# Should find checked/saturating alternatives instead
rg "checked_add|checked_sub|checked_mul|checked_div" pallets/
rg "saturating_add|saturating_sub|saturating_mul" pallets/

Step 5: Weight Analysis

  • Run benchmarking: cargo test --features runtime-benchmarks
  • Verify weights match computational cost
  • Check for bounded input parameters
  • Review weight calculation functions

Step 6: Origin & Privilege Review

bash
# Find privileged operations
rg "ensure_signed" pallets/ | grep -E "pause|emergency|admin|force|sudo"

# Should use ensure_root or custom origins
rg "ensure_root|ForceOrigin|AdminOrigin" pallets/

Step 7: Testing Review

  • Unit tests cover all dispatchables
  • Fuzz tests for panic conditions
  • Benchmarks for weight calculation
  • try-runtime tests for migrations

Step 8: Report Coverage

Report on every pattern in §5, whether or not it turned anything up. This skill has no Finding Template, so the table opens the report and the findings follow it, with all 7 rows present:

#PatternVerdictEvidence
1Arithmetic Overflowfoundsrc/lib.rs:212 -- + on BalanceOf<T> in do_transfer
2Don't Panic
3Weights and Fees
4Verify First, Write Last
5Unsigned Transaction Validation
6Bad Randomness
7Bad Origin

Each verdict is one of:

  • found — cite file:line and write the finding up in full after the table.
  • clear — the pattern applies to this pallet and the pallet handles it. Name the macro, origin check, or arithmetic method you searched for, so a reader can repeat the search.
  • n/a — the pattern cannot apply here. Give the reason in one clause ("this pallet accepts no unsigned transactions"). Not having looked is not n/a. Note that pattern 4 is version-scoped (pre-v0.9.25): say which runtime version the pallet targets rather than dropping the row.

A table with fewer than 7 rows is an incomplete scan and must be reported as one. A row whose Verdict cell is empty is incomplete in the same way: row 1 above is filled in to show the shape, and every row is filled in the same way before the report is done. Seven clear verdicts is a result a reader can act on. A report that covers three patterns and says nothing about the other four reads exactly like a clean pallet, and that is the failure this table exists to prevent.


7. Priority Guidelines

Critical (Immediate Fix Required)

  • Arithmetic overflow (token creation, balance manipulation)
  • Panic DoS (node crash risk)
  • Bad origin (unauthorized privileged operations)

High (Fix Before Launch)

  • Incorrect weights (DoS via spam)
  • Verify-first violations (state corruption, pre-v0.9.25)
  • Unsigned validation issues (spam, replay attacks)

Medium (Address in Audit)

  • Bad randomness (manipulation possible but limited impact)

8. Testing Recommendations

Fuzz Testing

rust
// Use test-fuzz for property-based testing
#[cfg(test)]
mod tests {
    use test_fuzz::test_fuzz;

    #[test_fuzz]
    fn fuzz_transfer(from: AccountId, to: AccountId, amount: u128) {
        // Should never panic
        let _ = Pallet::transfer(from, to, amount);
    }

    #[test_fuzz]
    fn fuzz_no_panics(call: Call) {
        // No dispatchable should panic
        let _ = call.dispatch(origin);
    }
}

Benchmarking

bash
# Run benchmarks to generate weights
cargo build --release --features runtime-benchmarks
./target/release/node benchmark pallet \
    --chain dev \
    --pallet pallet_example \
    --extrinsic "*" \
    --steps 50 \
    --repeat 20

try-runtime

bash
# Test runtime upgrades
cargo build --release --features try-runtime
try-runtime --runtime ./target/release/wbuild/runtime.wasm \
    on-runtime-upgrade live --uri wss://rpc.polkadot.io

9. Additional Resources


10. Quick Reference Checklist

Before completing Substrate pallet audit:

Arithmetic Safety (CRITICAL):

  • No direct +, -, *, / operators in dispatchables
  • All arithmetic uses checked_* or saturating_*
  • Type conversions use try_into() with error handling

Panic Prevention (CRITICAL):

  • No unwrap() or expect() in dispatchables
  • No direct array/slice indexing without bounds check
  • All user inputs validated with ensure!
  • Division operations check for zero divisor

Weights & DoS (CRITICAL):

  • Weights proportional to computational cost
  • Input parameters have maximum bounds
  • Benchmarking used to determine weights
  • No free (zero-weight) expensive operations

Access Control (CRITICAL):

  • Privileged operations use ensure_root or custom origins
  • ensure_signed only for user-level operations
  • Origin types properly configured in runtime
  • Sudo pallet removed before production

Storage Safety (HIGH):

  • Using Substrate v0.9.25+ OR manual #[transactional]
  • Validation before storage writes
  • Events emitted after successful operations

Other (MEDIUM):

  • Unsigned transactions use signed alternative if possible
  • If unsigned: proper validation, replay protection, authentication
  • BABE randomness used (not RandomnessCollectiveFlip)
  • Randomness uses random(subject) not random_seed()

Testing:

  • Unit tests for all dispatchables
  • Fuzz tests to find panics
  • Benchmarks generated and verified
  • try-runtime tests for migrations
  • Coverage table emitted with all 7 rows, each carrying a verdict of found, clear or n/a with a reason

11. Rationalizations to Reject

  • "The pallet is small, so most patterns obviously don't apply." Obvious to whom? An n/a costs one clause and makes the judgment reviewable. Silence records nothing, and a reader cannot tell it apart from not having checked.
  • "It compiles without warnings, so the arithmetic is fine." Release builds wrap silently. + on a Balance is pattern 1 whether or not the compiler said anything, and debug assertions do not run on a production node.
  • "I checked the patterns that matter for this pallet." Deciding which patterns matter is the scan, not a precondition for starting it. Rank by severity after the table is complete, not by leaving rows out.
  • "No findings, so there is nothing to report." A zero-finding scan still emits the full coverage table. That table is the deliverable: it is what distinguishes a pallet that was examined from one that was glanced at.
  • "ensure_signed is an origin check." It establishes that someone signed, not that the right someone did. Pattern 7 is about which origin is required, and ensure_signed where ensure_root belongs passes this rationalization while failing the check.
  • "The weight is benchmarked, so it's correct." Benchmarks measure the path they exercise. A dispatchable whose worst case depends on storage size or an unbounded loop is pattern 3 even with a benchmark attached.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Substrate Vulnerability Scanner AI skill do?

Scans Substrate/Polkadot pallets for 7 critical vulnerabilities including arithmetic overflow, panic DoS, incorrect weights, and bad origin checks. Use when auditing Substrate runtimes or FRAME pallets.

Why use Substrate Vulnerability Scanner on TypingMind?

Because you install it once and use it with any model. Substrate Vulnerability Scanner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Substrate Vulnerability Scanner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/building-secure-contracts/skills/substrate-vulnerability-scanner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Substrate Vulnerability Scanner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Substrate Vulnerability Scanner?

As many as you like. As long as a model supports skills, you can use Substrate Vulnerability Scanner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Substrate Vulnerability Scanner AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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