Dos

CVE-2025-58157: Denial-of-Service in ConsenSys gnark ZK-SNARK Library

Amit Schendel

Amit Schendel

— 6 min read

Zero-Knowledge Proofs (ZKPs) feel like magic. They let you prove you know a secret without revealing the secret itself. At the heart of this magic are powerful cryptographic libraries like Consensys's gnark, which handles the incredibly complex math required. But what happens when a tiny flaw in that math can be exploited to trap a system in an infinite loop?

Enter CVE-2025-58157, a denial-of-service vulnerability that turns a clever optimization into a computational black hole. An attacker can craft a special input that sends a gnark prover into a cryptographic coma, endlessly crunching numbers without ever reaching a conclusion. Let's dive into how this "Loop of Doom" works and how to escape it.

TL;DR / Executive Summary

  • Vulnerability: Denial of Service (DoS) in the gnark ZK-SNARK library.
  • CVE ID: CVE-2025-58157
  • Affected Systems: gnark version v0.12.0.
  • Impact: A malicious prover can provide a specially crafted witness that causes the system to enter a very slowly converging (effectively infinite) loop during a cryptographic computation. This freezes the proving process, wasting massive computational resources.
  • Root Cause: The "fake-GLV" algorithm, an optimization for scalar multiplication, fails to handle certain inputs (like negative scalars) correctly within its dependency, gnark-crypto. The underlying Half-GCD algorithm fails to converge, leading to the loop.
  • Mitigation: Update gnark to version v0.13.0 or later. This is a simple dependency update and does not require recompiling your circuits.

Introduction: When Optimizations Attack

In the world of ZKPs, performance is king. Proving a statement can be computationally expensive, so developers are always looking for clever shortcuts to speed things up. One such shortcut is the Gallo-Longa-Vercauteren (GLV) method, which dramatically accelerates a core operation called elliptic curve scalar multiplication.

Think of scalar multiplication as taking a specific number of steps along a winding, complex path on a map (the elliptic curve). The GLV method is like discovering a secret teleporter that lets you take a few giant leaps to get to your destination much faster.

But not all curves have the right properties for this "teleporter." For those that don't, gnark implemented a clever alternative called the "fake-GLV" method. It's a brilliant piece of engineering that mimics the speed benefits of GLV. However, as with any complex machinery, a single loose bolt can bring the whole thing grinding to a halt. CVE-2025-58157 is that loose bolt.

Technical Deep Dive: The Math Behind the Mayhem

To understand this bug, we need to look at how the "fake-GLV" method works.

  1. The Goal: Speed up the computation of s * P, where s is a large number (a scalar) and P is a point on an elliptic curve.
  2. The Method: The fake-GLV technique breaks the large scalar s into two smaller components, k1 and k2. The core idea is to find these components such that s = k1 + k2 * λ, where λ is a special value associated with the curve.
  3. The Problem: Finding k1 and k2 is a mathematical puzzle called scalar decomposition. gnark uses an algorithm known as Half-GCD for this task, which lives in its gnark-crypto library.

Root Cause Analysis

The vulnerability lies squarely in this Half-GCD implementation. It's like a GPS designed to find the fastest route between two points. For most destinations, it works perfectly. But for certain inputs—specifically, negative scalars—the GPS gets confused. It enters a state where it keeps recalculating the route, each time making minuscule progress but never actually reaching the destination. The algorithm fails to converge.

When an attacker provides a witness containing one of these "poisonous" negative scalars, the gnark prover dutifully calls the Half-GCD function to perform the decomposition. The function enters its near-infinite loop, consuming 100% of a CPU core and never returning. The prover is effectively frozen, resulting in a Denial of Service.

Attack Vector and Business Impact

The attack vector is straightforward: any system using gnark v0.12.0 that accepts witness data from an untrusted source is vulnerable.

  • Attack Scenario: An attacker participates in a protocol that requires them to generate a ZK-proof. They craft a witness where a scalar multiplication operation uses a value like -2. They submit this to the prover. The prover, attempting to generate the proof, gets stuck, and the service it supports is halted.
  • Impact:
    • Denial of Service: The most direct impact. Legitimate users are blocked from using the service.
    • Resource Exhaustion: The looping process consumes significant CPU time, leading to high costs in cloud environments and starving other processes of resources.
    • System Instability: In a distributed system like a blockchain rollup, a frozen prover can disrupt batch processing, delay transactions, and undermine the network's reliability.

Proof of Concept (PoC)

We can demonstrate this vulnerability conceptually. The patch itself gives us the biggest clue by adding a test case for a negative scalar.

An attacker would create a circuit that performs scalar multiplication and then provide a malicious witness to the prover.

// Theoretical PoC - Illustrates the vulnerable interaction

// 1. The Circuit Definition (in a vulnerable project)
type MyCircuit struct {
    Point    frontend.Variable
    Scalar   frontend.Variable
    Expected frontend.Variable `gnark:",public"`
}

func (c *MyCircuit) Define(api frontend.API) error {
    // This is the call that triggers the vulnerable "fake-GLV" hint function
    result := api.ScalarMul(c.Point, c.Scalar) 
    api.AssertIsEqual(result, c.Expected)
    return nil
}

// 2. The Attacker's Malicious Witness
// The attacker crafts a witness where `Scalar` is a problematic value.
// The patch added a test for -2, so let's use that.
maliciousWitness := MyCircuit{
    Point:    someBasePoint,
    Scalar:   -2, // The "poison pill" for the Half-GCD algorithm
    Expected: someExpectedResult,
}

// 3. The Prover Gets Stuck
// When the prover attempts to generate the proof for this witness...
// prover.Prove(circuit, maliciousWitness, ...)
// ...the process will hang indefinitely, consuming a full CPU core.

The api.ScalarMul function is the entry point. Under the hood, for certain curves, it delegates the hard work to the fake-GLV implementation, which in turn calls the buggy scalar decomposition function in gnark-crypto, triggering the infinite loop.

Mitigation and Remediation

Thankfully, fixing this is straightforward. The flaw isn't in the circuit logic but in the underlying cryptographic library.

Immediate Fix:

Update your go.mod file to use gnark version v0.13.0 or newer.

# Get the latest version of gnark
go get github.com/consensys/gnark@latest
# Tidy up dependencies
go mod tidy

This single change pulls in the patched version of the gnark-crypto dependency, and the vulnerability is resolved. No circuit recompilation is necessary.

Patch Analysis:

The fix itself is a beautiful example of targeted, minimal change. The main gnark repository was updated with a single commit (68be6cede...) that does one thing: it updates the required version of gnark-crypto.

  • File: go.mod
    -	github.com/consensys/gnark-crypto v0.17.1-0.20250415081852-c838dcdfa844
+	github.com/consensys/gnark-crypto v0.17.1-0.20250502112255-56600883e0e9

The actual code fix is in the gnark-crypto repository (commit 56600883e0e9...), where the Half-GCD algorithm was corrected to properly handle the edge cases that caused the non-convergence. To validate the fix, the developers also added a new test case in point_test.go that specifically uses a negative scalar (-2), ensuring this bug can't creep back in. This is a textbook example of good security patching and regression testing.

Timeline

  • Discovery: May 1, 2025 - The issue was reported by researcher @feltroidprime on the gnark GitHub repository.
  • Vendor Notification: May 1, 2025 - The GitHub issue served as the official notification.
  • Patch Availability: May 2, 2025 - The Consensys team quickly patched the gnark-crypto library and updated the main gnark repository.
  • Public Disclosure: August 29, 2025 - The CVE was publicly disclosed.

Lessons Learned

This vulnerability, while subtle, teaches us a few critical lessons.

  1. Optimizations are a Major Attack Surface: The drive for performance can introduce complex code paths that are ripe for exploitation. The "fake-GLV" method is a powerful optimization, but its complexity hid a critical flaw. Always treat optimized code with an extra layer of scrutiny.
  2. Validate Everything, Especially in Crypto: Cryptographic primitives often assume well-behaved inputs. This bug is a stark reminder that in a security context, you must validate and test for all possible inputs, including negative numbers, zero, and other edge cases. Fuzzing mathematical libraries is not just a good idea; it's essential.
  3. Monitor Your Provers: From a detection standpoint, this type of bug manifests as a performance anomaly. A prover taking an abnormally long time to complete a task should be a red flag. Implementing timeouts and monitoring execution metrics can help catch these "algorithmic complexity" attacks before they cause a full denial of service.

Key Takeaway: The magic of cryptography rests on a foundation of precise, predictable mathematics. When that foundation has a crack—even a tiny one—the entire structure is at risk.

So, the next time you're implementing a complex algorithm, ask yourself: have I tested the weird cases? Because in security, the weird cases are exactly where the attackers live.

References and Further Reading

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