Live now: Quadratic Funding Round

Supported by EF Security DAO x Giveth Round

LeakDetect: Protecting Ethereum from Hidden Leakages is part of the Ethereum Security Quadratic Funding Round on Giveth. 500 ETH secured to help grow the pool. Support the initiative by donating and following our updates.

View on Giveth Read the Giveth updates X Updates

EF Security DAO x Giveth Quadratic Funding Round: Ethereum Security, 500 ETH secured, help grow the pool

@manusheel · Ethereum security public goods

X Updates

From Ethereum Security QF support to our submitted EF Security DAO grant proposal: the public story behind LeakDetect.ai.

EF Security DAO grant proposal

01 · QF ROUND ACCEPTANCE · 12 MAY 2026

Security under observation.

Grateful to Giveth and The DAO Fund Security Fund for supporting open-source Ethereum security research. LeakDetect.ai was officially accepted into the Ethereum Security QF Round.

Ethereum wallets, Web3 applications, AI agents and multi-chain systems can reveal secrets even when they work correctly. Transaction timing, gas usage, signing behavior and cross-chain execution traces can expose hidden information without an attacker exploiting a bug.

Research, open-source foundations & funding goals

Visible threats include smart contract exploits, wallet hacks and phishing. Information leakage is a subtler risk, grounded in decades of side-channel and cryptographic research, including Bleichenbacher attacks, TLS padding oracles and timing attacks.

As AI agents manage wallets, sign transactions and coordinate across chains, autonomous asset management makes this risk increasingly urgent. Current tooling cannot measure everything systems unintentionally reveal.

An open-source framework to

Detect hidden leakage and simulate real adversaries.
Analyze timing, gas and API signals.
Apply machine learning to inference attacks.
Quantify security leakage using formal metrics.

The work builds on years of collaborative research, with encouragement and support from Ruhr-Universität Bochum, Software Innovation Campus Paderborn (SICP) and N.S.U.T., New Delhi, India.

Released foundations include Deep Learning SCA, AutoMLQuantILDetect, AutoML QILD Experiments and AutoSCA HW Wallet Tester, including TimesFM and AutoKeras for automated side-channel analysis.

What the funding will help build

A production-ready SDK and CLI, with EtherCalc testing.
Public leakage benchmarks and datasets.
Wallet and AI agent integrations.
Standardized leakage security metrics.
Educational resources and workshops.

Fully open-source for the ecosystem. Leakage vulnerabilities are a public-goods problem affecting Ethereum, Web3, AI agents and multi-chain systems. Quadratic funding lets small donations contribute through matching.

Read the acceptance thread on X

02 · FOLLOWING THE ETH SECURITY QF ROUND

Leak Detect: Protecting ETH, Web3 from Hidden Leakages

Following the ETH Security QF round, we’re grateful for the support in direct donations and ETH from the matching pool.

Thanks to @TheDAOfund, @wintermute_t and everyone who supported us!

Building toward the EF Security DAO grant

Community support and public research updates provide context for the submitted proposal: reproducible leakage detection, public benchmarks, wallet and AI agent integrations, and practical security metrics.

QF support and the EF Security DAO grant application are distinct. The grant proposal is submitted, not an announcement of grant approval.

Full project update ↗Giveth updates ↗Project on Giveth ↗

Read the support update on X

Proposal · Ethereum Trillion Dollar Security (1TS) Initiative

Security under observation.

LeakDetect.ai is an open-source framework that detects, measures, and simulates hidden information leakage across Ethereum wallets, AI agents, smart contracts, and multi-chain systems.

Explore the Security Stack About 1TS ↗

leakdetect ~ observability-trace · wallet 0xA1c…7e3

live

Vulnerability Score

0.78

high

Key Recovery Probability

0.42

warn

Information Leakage Rate

1.6 b/s

warn

→ capture timing gas signing rpc xchain

→ simulate behavioral_inference on 12,488 traces

→ infer side_channel via deep-learning ensemble

! leakage detected: signing-time variance correlates with nonce parity (p < 0.001)

TIMING_VARIANCEGAS_PATTERNNONCE_PARITYRPC_FINGERPRINTSIGN_CADENCEBRIDGE_RELAYAGENT_TRACEMETADATA_DRIFTKEY_RECOVERY_PROBSTEALTH_LEAK_INDEXTIMING_VARIANCEGAS_PATTERNNONCE_PARITYRPC_FINGERPRINTSIGN_CADENCEBRIDGE_RELAYAGENT_TRACEMETADATA_DRIFTKEY_RECOVERY_PROBSTEALTH_LEAK_INDEX

// paradigm shift

Instead of asking "can this system execute safely?" LeakDetect.ai asks: what does this system unintentionally reveal while operating?

01: the problem

The threats no one is watching.

Ethereum security today focuses on visible failures: contract exploits, phishing, wallet compromise. But systems can function correctly while unintentionally leaking sensitive information.

Adversaries with deep learning, statistical inference, and side-channel analysis are turning observation itself into the attack surface.

Transaction timing

Microsecond variance reveals nonce entropy and signer state.

Gas usage patterns

Branch behavior in contracts becomes observable from receipts.

Signing behavior

HSM and wallet firmware leak through approval cadence.

Cross-chain execution traces

Relayers expose coordination metadata across L1/L2/L3.

API interaction patterns

RPC fingerprints uniquely identify wallets and agents.

Wallet metadata

Approvals, allowances, and dust create persistent fingerprints.

Autonomous agent coordination

Agent-to-agent flows leak strategy and intent.

Hardware side channels

Power, timing, and EM emissions remain underexamined on-chain.

02: alignment with 1ts

Mapped to Ethereum's trillion-dollar security themes.

ethereum.org/trillion-dollar-security ↗

UX & Blind Signingaligned

Signing-time leakage analysis
Wallet interaction observability
Approval-pattern fingerprinting
Autonomous signing behavior

Privacy & Metadata Exposurealigned

Behavioral deanonymization
Multi-chain traceability
Wallet fingerprinting
AI-agent operational inference

Cross-chain & Infrastructurealigned

Bridge interaction leakage
RPC metadata exposure
Cross-chain timing analysis
Infrastructure observability risk

AI-Generated Security Risksaligned

AI transaction agents
Autonomous execution frameworks
Agent-to-agent coordination
AI-generated contract flows

03: technical architecture

Five layers, one observability pipeline.

Signal Capture

Transaction timing, gas, signing behavior, wallet traces, network metadata, cross-chain relay timing.

Adversarial Simulation

Behavioral inference, timing correlation, cross-chain observability, AI-agent tracking attacks.

ML Inference Engine

Deep learning, NAS, AutoML, sequence analysis, time-series security modeling.

Leakage Quantification

Information leakage, key recovery probability, observability risk, operational fingerprinting.

Reporting & Visualization

Leakage dashboards, wallet risk analysis, agent observability maps, cross-chain reports.

04: quantification

Measurable security, not vibes.

LeakDetect.ai introduces standardized metrics so that observability risk can be tracked, benchmarked, and reduced across releases: the way performance and coverage already are.

0.00 – 1.00

Vulnerability Score

Overall exploitability estimate based on observable surface area.

KRP

0% – 100%

Key Recovery Probability

Likelihood of reconstructing secrets from leaked side-channels.

ILR

b/s

Information Leakage Rate

Amount of leaked information per unit of time, in bits/second.

SLI

index

Stealth Leakage Index

Difficulty of detecting the leakage by defenders and monitors.

05: research foundations

Eight years of leakage research, ported to Ethereum.

3y+

Open-source security research at the Research Center Trustworthy Data Science and Security, Ruhr-Universität Bochum.

5y+

Foundational AI and cryptographic security research at the Software Innovation Campus Paderborn (SICP).

→

Extends AutoSCA (ACM AISec, "Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!") into wallets, AI agents, and multi-chain systems.

deep-learning-sca

↗

Deep learning side-channel analysis toolkit.

github.com/SEC4AI-AI4SEC

AutoMLQuantILDetect

↗

AutoML quantitative information leakage detection.

github.com/SEC4AI-AI4SEC

automl-qild-experiments

↗

Reproducible leakage detection experiments and benchmarks.

github.com/SEC4AI-AI4SEC

autosca-hw-wallet-tester

↗

Hardware wallet security testing with modern deep learning: TimesFM forecasting and the latest AutoKeras releases for automated side-channel and security analysis.

github.com/SEC4AI-AI4SEC

Prior art: achelos/AutoSCA : ML-assisted leakage detection and real-world vulnerability pipelines that LeakDetect.ai builds upon.

06: research results

Benchmarked leakage detection, measured against baselines.

Results from the LeakDetect.ai research pipeline: AutoML based leakage detection on timing and error-code vulnerabilities, side-channel key recovery benchmarks, and validation against an industrial-grade TLS testing tool.

detection accuracy: timing based vulnerability

Technique	10/90	30/70	Balanced
AutoML Mid-Point	0.50	0.50	0.78
AutoML Log-Loss	0.50	0.66	0.74
DL-LA Baseline	0.46	0.49	0.50

AutoML estimators stay informative under class imbalance where the DL-LA baseline collapses toward random guessing.

detection accuracy: error-code based vulnerability

Technique	10/90	30/70	Balanced
AutoML Mid-Point	0.34	0.36	0.88
AutoML Log-Loss	0.88	0.91	0.93
AutoML Cal Log-Loss	0.87	0.91	0.94
AutoML Fischer's Exact Test	0.82	0.62	0.95
AutoML Paired t-Test	0.61	0.59	0.96
GMM Baseline	0.33	0.33	0.33
MINE Baseline	0.63	0.64	0.67
PC-Softmax Baseline	0.69	0.69	0.70
DL-LA Baseline	0.51	0.56	0.72

side-channel key recovery success rate (ARES 2022)

AES_RD

52% : 100%

AES_HD

38% : 60%

CHES CTF

2% : 2%

DPAv4

75% : 100%

ASCAD_f

46% : 100%

ASCAD_r

56% : 71%

ASCAD_f desync50

8% : 57%

ASCAD_r desync50

13% : 85%

ASCAD_f desync100

7% : 70%

ASCAD_r desync100

13% : 71%

All parameter combinations compared with 1D CNNs found by random search, across 4 systems with countermeasures and 6 without. Success rate is the share of attacks reaching guessing entropy of 1.

openssl tls evaluation

Vulnerable systems

Non-vulnerable systems

Validated by mapping the LeakDetect.ai approach onto the industrial AutoSCA test tool from Achelos GmbH.

autosca-tool ↗wallet tester results ↗full demo ↗

methodology

A model-agnostic leakage assessment score quantifies information leakage. AutoML based estimators provide a black-box automated detection path, scale to high-dimensional datasets, and remain precise on imbalanced data. Statistical gating uses one-sample t-test, Fischer's exact test and paired t-test with Holm-Bonferroni correction before a system is labelled vulnerable.

07: giveth update

LeakDetect: protecting Ethereum from hidden leakages.

We are grateful to everyone who supported LeakDetect.ai through the Ethereum Security QF round. The project received direct donations plus ETH from the matching pool, with special thanks to The DAO Fund, Wintermute and all individual contributors.

Advanced the leakage-detection methodology

An automated, black-box approach to detect, measure, reproduce, understand and mitigate hidden information leakage, including timing, metadata, network behaviour, communication patterns and other side channels.

Expanded from benchmarks to real-world ecosystems

Collecting and preparing datasets across Ethereum, Zcash, Tornado Cash, wallet security, P2P/libp2p and cross-chain workflows to validate whether the methodology generalizes across environments.

Started ETH and FIL security research

Investigating Ethereum to Filecoin swap workflows for potential leakage across transaction flows, interoperability, networking and observable system behaviour.

Extended research toward P2P/libp2p security

Moving beyond application-level signals toward network metadata, timing, peer interactions, routing and protocol behaviour, connecting directly with our libp2p, IPFS, IPLD and multiformats work.

Increased ecosystem engagement

Presented two Internet-Drafts and two research papers at IETF 126 in Vienna, recommended for an Ethereum 1TS proposal, with continued discussions across the Ethereum security ecosystem.

Broadened the research vision

The LeakDetect.ai plus IPFS/libp2p deck is now public and used as the shared reference for collaborators.

next milestones and use of QF funds

→Complete real-world dataset collection and preparation.
→Run automated leakage detection across Ethereum, Zcash, Tornado Cash, wallets and ETH-FIL workflows.
→Extend analysis to P2P/libp2p network-level leakage.
→Publish technical reports, datasets where appropriate, case studies and IETF materials.
→Document reproducible findings, leakage scores and practical mitigations.
→Refine milestones with Ethereum security and P2P contributors and continue the 1TS proposal.
→Explore a Trillion Dollar Security Research Retreat bringing researchers, protocol developers and security engineers together.

Our broader goal is simple: identify what decentralized systems unintentionally reveal, and develop practical, reproducible ways to detect and mitigate that leakage before it becomes a security or privacy problem.

resources

Research retreat (hybrid)↗Community feedback and enhancements↗Trillion Dollar Security proposal↗Updates, results and datasets↗

07: decentralized ai infrastructure

Scalable, private, and decentralized by default.

As LeakDetect.ai scales across wallets, agents, and chains, we are building the infrastructure to keep security intelligence open, privacy-preserving, and censorship-resistant: from storage to inference.

IPFSprivacy-preserving storage

Audit logs, inference traces, and leakage datasets are stored on IPFS with content-addressing and encryption. No single point of failure. No custodial data silos.

libp2pdecentralized networking

P2P gossipsub and DHT routing enable AI agents to share threat signals, model updates, and observability metadata without centralized relays. Resilient by design.

Nillionzk training & inference

Privacy-preserving computation for ML training and inference. Secrets are split into shares across a decentralized network: no node ever sees the full model or raw data.

Hugging Faceopen model ecosystem

Foundation models, tokenizers, and evaluation pipelines integrated natively. Fine-tune leakage-detection transformers and publish them back to the community.

zk pipeline overview

Capture

Signals from wallets, agents, chains

Encrypt & Shard

Nillion secret sharing

zk Train / Infer

Private ML on distributed shares

Publish

IPFS + libp2p propagation

08: what we plan to build

Open infrastructure for observability-aware security.

Production-ready toolkit

CLI tooling
SDKs
Wallet integrations
AI-agent integrations
Multi-chain test infra

Public benchmarks & datasets

Standardized leakage datasets
Open benchmark suites
Reproducible pipelines
AI-agent observability corpora

Ecosystem integrations

Wallet providers
AI-agent frameworks
Infrastructure providers
Security monitoring
Multi-chain dApps

Security standardization

Leakage-aware metrics
Wallet observability standards
Agent operational assessment
Cross-chain metadata risk

Decentralized AI infrastructure

IPFS for privacy-preserving log storage
libp2p for decentralized AI networking
Nillion zk training & inference pipeline
Hugging Face model integration & fine-tuning

09: partnerships & field testing

Collaborations powering observability-aware security in the wild.

Achelos / AutoSCA

Side-channel analysis tooling

www.achelos.de/en/ ↗

Aave

Enterprise AI agent observability

Aspiring INVESTMENTS CORP

Investment & venture partner

www.aspiringapps.com ↗

Agent0 (ERC-8004)

ERC-8004: Trustless Agents. Discover agents and establish trust through reputation and validation

Morpho

Health-AI observability pilots

Thales Security team led by Vibhor Bijoy

Security research & advisory

Samagra

Public-sector deployments

samagragovernance.in/ ↗

TickerValue

Partner platform & market intelligence

tickervalue.com ↗

Park Pro Token

Field testing partner & token deployment

park-token-deploy.vercel.app/ ↗

libp2p

Decentralized peer-to-peer networking for AI agents

libp2p.io/ ↗

IPFS

Privacy-preserving, content-addressed log storage

ipfs.tech/ ↗

Multiformats

Self-describing data formats for interoperability

github.com/multiformats ↗

IPLD

Linked data layer for distributed systems

github.com/ipld ↗

Public AI

Open, verifiable AI infrastructure

publicai.co/ ↗

SAP BTP & Joule Team

Enterprise AI agent observability

Davos Economic Forum Committee: New Delhi

Policy & global security dialogue

Diagnosity

Health-AI observability pilots

field deployment

Park Pro Token on Ledger

Live testing on Park Pro Token (PPT) using a Ledger hardware wallet
PPT is listed across 10+ key EVM chains on SushiSwap and Mainnet
Real-world wallet, signing and cross-chain leakage telemetry feeds the detector

EVM · SushiSwap · Mainnet · Ledger

10: sunstone x leakdetect.ai

Real-time leakage detection for AI agents.

Sunstone: a solar-powered IoT emergency pendant for elderly individuals living alone: is a key government-affiliated project that has added LeakDetect.ai to its roadmap. The same temporal-anomaly principle Sunstone uses to catch a medical emergency from continuous sensor data is being applied to live AI-agent behavioral streams on Ethereum.

what gap does this address

LeakDetect.ai today profiles a system, trains on its traces, and measures leakage: ideal for controlled hardware devices and TLS servers. AI agents executing autonomous transactions on Ethereum are different: they emit signals continuously, and an adversary won't wait for a profiling phase to finish. The Sunstone roadmap brings a streaming detector that learns normal behavior in place and raises a flag the moment something drifts.

Phase 1

Understanding & simulating agent behavioral signals

Identify the behavioral signals AI agents emit: transaction timing, gas usage per operation, signing behavior, API call sequences, response-time variance: and structure them as time-series streams. Synthesize a standardized Ethereum-agent dataset of normal sequences plus injected leakage patterns, in the same spirit as ASCAD / AES_HD for hardware. Statistically profile the normal sequences to establish a baseline the live detector can reason against.

Phase 2

Baseline temporal model

Validate the core idea: can a temporal model watching a live stream of agent behavioral signals detect leakage-shaped deviations? A Bidirectional LSTM consumes a sliding window of readings, trains only on normal sequences, and flags anything outside that envelope. BiLSTM reads context in both directions, which helps separate true anomalies from noise. Output: a Python PoC that ingests a simulated stream, learns normal, and raises real-time flags as new data arrives.

Phase 3

Attention over the temporal model

Different channels carry different signal depending on the leak: timing for timing leaks, gas for computation-heavy leaks. An attention layer on top of the BiLSTM learns per-channel and per-timestep weights, focusing the model on the most informative slices of the sequence. The weighted summary feeds the anomaly scoring head. Benchmarked against the Phase 2 baseline on detection accuracy and false-positive rate.

Phase 4

Integration with LeakDetect.ai

The temporal detector sits alongside the existing profiling pipeline as a live monitoring layer: offline benchmarking stays, real-time flagging is added. A lightweight collection interface captures agent signals as time-series windows. Anomaly scores are mapped onto the existing VS / KRP / ILR vulnerability framework so outputs are consistent. Initial benchmarks on simulated Ethereum-agent scenarios give comparable numbers to the hardware and TLS suites already in the repo.

// collaborate with us

In decentralized systems, anything observable can eventually become exploitable.

We invite Ethereum researchers, wallet providers, AI-agent developers, infrastructure builders, and multi-chain teams to collaborate with LeakDetect.ai in advancing the next generation of Ethereum security.

marketing@tickervalue.com

Email the team →Join the Discord ↗View on GitHub ↗