Proposal: DamlSec, Automated Security Analysis of DAML Contracts (RFP 22) - resubmission of #194

Development Fund Proposal - DamlSec: Automated Security Analysis of DAML Contracts via Symbolic Execution

Author: Dedaub Submitted by: Giorgio Bonuccelli (@georgeiv76) Technical representative: Neville Grech (@nevillegrech) Status: Submitted Created: 2026-04-09 (resubmitted under Dev Fund 2.0, November 2026) Supersedes: PR #194, closed October 22, 2026 during the Dev Fund 2.0 transition RFP: 22. Daml Security Standards and Secure Development (primary); 3. Automated Application Management (secondary) Label: daml-tooling SIG: Security; Daml Language & Developer Tooling Champion: Need Champion

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RFP Alignment and Ecosystem Need

This section answers the two items the Dev Fund 2.0 notice asked for: the RFP category and why the ecosystem needs the tool. It restates the proposal below and adds no new technical commitment.

RFP category

Primary: Daml Security Standards and Secure Development (RFP 22). The RFP asks for "automated analysis, security-focused linting or static analysis" that helps "application developers consistently identify and prevent security weaknesses before Daml packages are deployed or vetted". DamlSec is a command-line tool and library that ingests a compiled .dar file, symbolically executes the choices of each template, and automatically reports violations of security properties, with counterexamples (Specification, sections 1 and 2.4).

Secondary: Automated Application Management (RFP 3). That RFP covers "review & security analysis" of Daml packages before a party vets them. DamlSec analyzes .dar files, not source code, including library code that a developer imports (Specification, section 3).

Who benefits

• Daml developers: the tool fits the artifact-deployment pipeline: generate a .dar, run damlsec analyze, gate deployment on finding-free output (section 3). It ships as a CLI, a Scala library, and a GitHub Actions workflow template (section 2.6).
• Auditors and security researchers: it reduces the cost and increases the thoroughness of Canton smart contract audits (section 3). Dedaub will incorporate DamlSec into its own DAML audit workflow and document the integration as a reference for other audit firms (Co-Marketing).
• Every project deploying DAML contracts on Canton: all benefit from the same security properties the tool checks; it is open source and designed for community extension (Motivation).

How it drives adoption

Security and auditing tooling is among the most requested categories in the Canton Network Developer Experience and Tooling Survey Analysis (2026), cited in Motivation. Manual audit does not scale and gives no systematic path enumeration (Motivation, landscape table). DamlSec is implemented in Scala, the same language as the Canton node and the Speedy interpreter, so a Canton contributor can read and modify it (section 3). Milestone 3 includes at least one real Canton ecosystem project running DamlSec and reviewing the results, in coordination with the Foundation.

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Abstract

DAML contracts securing trillions of dollars of institutional assets on Canton have virtually no automated adversarial security tooling. Manual audits do not systematically enumerate all paths through contract logic or prove that authorization, conservation, and arithmetic properties hold across every feasible execution. DamlSec fills this gap with a symbolic execution engine purpose-built for DAML-LF, Canton's typed intermediate representation.

The engine executes contracts symbolically - treating inputs, party identities, and ledger state as unknown quantities - and uses a two-tier SMT strategy: a pure-Scala theorem prover called from the hot loop to quickly prune infeasible paths, and CVC5 at terminating paths to discharge the heavier security property obligations and extract concrete counterexamples. CVC5 support will be implemented in such a way to have its native dependency optional in the build, so DamlSec can be distributed and run with the inner prover alone for environments where ease of installation and distribution is paramount. The inner prover primarily decides quantifier-free linear integer arithmetic with uninterpreted functions (QF_LIA + UF) - matching most DAML security obligations: party identity comparisons, authorization set membership, amount conservation equalities. Being pure Scala and JVM-native, it has zero FFI overhead and no native-library fragility, making it faster than architectures employing external solvers and much easier to distribute along the standard DAML toolchain.

Dedaub brings directly applicable depth to this project: our team has audited Canton/DAML projects, has senior staff with extensive Haskell and Scala production experience, and a strong background in program analysis, as evidenced by our publication track record and static analysis tools in the Ethereum space. We already have a working prototype which executes for simple DAML-LF programs. This proposal funds the development of the project, security property coverage, and open-source release. Dedaub commits to maintaining the project for two years post-completion. DamlSec will be implemented purely in Scala, the same language as the Canton node, the Speedy interpreter, and the broader Digital Asset stack, ensuring it is easy for the community to understand, contribute to, and extend.

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Specification

1. Objective

DAML's type system and authorization model prevent whole categories of smart contract vulnerabilities present in Solidity. But they do not prevent everything. Dedaub's audit work on Canton projects documented recurring vulnerability classes that do not produce compilation errors; they are logic errors that require reasoning about all feasible execution paths simultaneously. No currently available tool does this for DAML.

We propose DamlSec, a command-line tool and library that ingests a compiled .dar file, symbolically executes the choices of each template, and automatically reports violations of security properties. The tool is fully automated - no annotations or specifications are required from the developer. It reports findings with counterexample assignments of concrete values that trigger the violation, making results immediately actionable. The tool will be open sourced and can be easily integrated into the DAML SDK workflow via damlc-compatible input, and documented for use by DAML developers, auditors, and security researchers.

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2. Implementation Mechanics

2.1 Architecture Overview

The implementation has three components, each directly reusing or extending existing Canton/DAML infrastructure.

.dar file
  └─► DAR Parser (Digital Asset's existing Scala library)
        └─► Package: (Ast.scala types)
              └─► Speedy Compiler  (Compiler.scala, unchanged)
                    └─► SExpr (closure-converted ANF form)
                          └─► DamlSec Symbolic Engine
                                ├─► SymState machinery and harness
                                ├─► Pure-Scala theorem prover (QF_LIA + UF;
                                │    fast path pruning, NNF, simplification)
                                ├─► CVC5 (end-of-path - full theory; security
                                │       property obligations + counterexamples;
                                │       native dep optional in build)
                                └─► Security Property Checkers
                                      └─► Findings report (JSON + human-readable)

2.2 Reusing the Speedy Compiler Pass

The Daml compiler's Speedy.Compiler translates Ast.Expr (the raw AST) into SExpr, a closure-converted, ANF-normalized form. This pass is production-grade, covers the full DAML-LF expression language, and is already tested against the complete DAML standard library and ecosystem contracts. DamlSec takes it as a library dependency without modification. This infrastructure is a very expensive engineering component in any static analysis or symbolic execution engine for a functional language, and unlike other approaches will not be re-implemented.

2.3 The Symbolic Execution Engine

The engine is a direct generalization of Speedy's execution model. Speedy maintains a machine state that is concrete and mutable. DamlSec replaces the concrete SValue domain with a symbolic SymValue domain and the single mutable machine state with (potentailly) hundreds of millions viable symbolic states. We make heavy use of persistent data structures - (e.g., HAMTs) and libraries such as Bifurcan - so that forking a state at a branch point shares the bulk of the data rather than copying it, keeping memory and allocation cost proportional to the actual divergence between paths rather than the total state size. In addition SymValue is a much more complex structure than the SValue structure using in DAML's reference implementation, and internally reflects symbolic formulae that are ameanable to lightweight theorem proving.

Our current prototype couples symbolic values directly with the inner prover's own term and formula types rather than a custom AST, in order to piggy-back on its built-in simplifier (constant folding, NNF rewriting, identity elimination, linear-arithmetic canonicalization) for free, on the same JVM, in the same call chain as the symbolic interpreter.

Inputs to a choice (the arg payload, the self contract, the caller party) are initialized as fresh symbolic constants of the appropriate sort, constrained only by the template's ensure clause (which is checked at creation time and encoded as a path condition assumption).

The engine processes a worklist of viable SymState values, and implements a symbolic semantics of SExpr.

DamlSec intends to use a deliberate two-tier SMT approach, exploiting the structural difference between path pruning (very high frequency, must be sub-millisecond) and optional property verification (low frequency). To achieve a high frequency SMT capability, we use a prover optimized for QF_LIA + UF (a simple logic, but one that very fast to discharge). The astute reader might point out that DAML arithmetic includes multiplication and integer division, which take us out of pure linear arithmetic. DamlSec resolves this by abstracting these as uninterpreted functions. This can be made to be sound for the path-pruning use case: an uninterpreted-function abstraction may fail to prune some infeasible paths that a richer theory would have caught, but it never incorrectly prunes a feasible path (no soundness loss). We also note that in practice, the bulk of DAML security-relevant feasibility is expressible within this logic authorization set membership, amount conservation comparisons, party identity.

For the Outer tier SMT, we're currently planning to use CVC5, which in the context of a Scala-based application is easier to maintain, due to better designed bindings.

Finally, an "abstract-interpretation" pre-analysis using declarative program analysis techniques over the program can identify which templates and choices can reach a security-sensitive operation (sinks), pruning unexplored paths away from sinks.

All of these architectural choices are deliberate - in order for DamlSec to be a practical, long-lasting tool it needs to be designed with more requirements in mind that what we normally assume: scaling to large programs, support the entirety of DAML semantics, with zero or very few false positives. It needs to be easy to distribute and run, and be written using the same technologies and dependencies as the rest of the DAML toolchain so as to attract people from the community to contribute and maintain it.

2.4 Security Property Checkers

Multiple security detectors will be implemented as observers on the symbolic effect trace, evaluated for each terminating path. As an informal illustration of the kinds of properties DamlSec will check:

• Conservation. When a contract transfers, splits, or merges value-bearing assets, the total amount in must equal the total amount out on every path. A conservation detector flags any execution where the symbolic engine can produce a path that creates assets out of nothing or silently destroys them, often a sign of a sloppy split/merge or an unguarded edge case.
• Authorization. Every contract action in DAML must be authorized by a specific set of parties. An authorization detector checks that every reachable creation, exercise, or archival is genuinely backed by the signatories the template demands - catching cases where a path lets one party act unilaterally on something that should require several.
• Arithmetic safety. Detects paths along which a division by zero, an overflow, an underflow, or a material rounding discrepancy can be triggered with reachable inputs. Rounding is especially relevant in DAML's financial domain. The symbolic engine reasons about the actual constraints on inputs, so this is much more precise than syntactic pattern matching for unguarded arithmetic.
• Privacy / divulgence. Flags paths along which a contract exposes data to a party that should not have visibility into it, an issue specific to DAML's per-party privacy model.
• Key and maintainer consistency. For templates that use contract keys, checks that the maintainers of every key are also signatories on every creation path, preventing classes of key/state desync bugs.

Each checker is merely a Scala function. Many more can be added, removed, or overridden independently. The architecture will be open for community contribution of new detectors without modifying the engine core.

2.5 Output Format

Findings are reported as structured JSON (for CI/audit tooling integration) and human-readable text. Each finding includes: the template name, choice name, detector name, the violating property as a readable formula, and a counterexample - when CVC5 is present, a concrete assignment of symbolic variables (contract field values, choice argument values, party identities), demonstrating exactly what input triggers the violation. Without CVC5, the inner prover still produces models for the linear-arithmetic fragment, covering the majority of findings.

2.6 Tooling Integration

DamlSec is going to be packaged as:

• A standalone CLI: damlsec analyze MyContracts.dar
• A Scala library (Maven/Gradle artifact) for programmatic integration in audit pipelines
• A GitHub Actions workflow template for CI integration

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3. Architectural Alignment

DamlSec is structurally aligned with the Canton/DAML stack in a way that no alternative approach could hope to achieve.

Same runtime model. It piggybacks on the production DAML-LF runtime system. When the DAML runtime evolves, DamlSec inherits these changes and needs only to add corresponding cases to the symbolic interpreter. This is the tightest possible coupling between a security tool and the runtime it analyzes.

Same language. DamlSec is implemented in Scala, the same language used by the Canton node, the Speedy interpreter, the Engine, and the ledger layer. There is no FFI boundary, no language mismatch, no separate build system. A Canton contributor familiar with the Canton source can read and modify DamlSec immediately. Even the inner theorem proving engine is in pure Scala.

Operates on compiled artifacts. DamlSec analyzes .dar files, not source code. This means it catches security issues that survive compilation, analyses library code that a developer imports (not just their own templates), and is immune to source-level obfuscation. It fits naturally into the artifact-deployment pipeline: generate a .dar, run damlsec analyze, gate deployment on finding-free output.

Aligned with CIP-0082 priorities. CIP-0082 explicitly identifies security, audits, and developer tooling as fund priorities. DamlSec is a security tool that also provides auditor tooling - it directly reduces the cost and increases the thoroughness of Canton smart contract audits, a common-good benefit for every project deploying on the network.

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4. Backward Compatibility

DamlSec is a standalone analysis tool that reads .dar files and produces reports. It does not modify the Canton protocol, the DAML runtime, any existing contract, or any ledger API. It introduces no compatibility obligations on existing projects. Adoption is purely opt-in.

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Milestones and Deliverables

Milestone 1: Core Symbolic Engine

• Estimated Delivery: 6 weeks from project start
• Focus: A complete, tested symbolic execution engine covering the DAML-LF expression language. The existing prototype is hardened into a production-grade implementation with test coverage against representative DAML contracts.
• Deliverables:
  • Open source repository with CI pipeline
  • Symbolic engine passing the DAML expression-language test suite
  • Demonstrated symbolic execution of representative real-world templates
  • Technical blog post describing the engine architecture

Milestone 2: Security Property Detectors and Counterexample Generation

• Estimated Delivery: 10 weeks from project start
• Focus: A first set of security property detectors implemented, integrated, and validated against real Canton contract vulnerabilities. Counterexample extraction produces human-readable, actionable output.
• Deliverables:
  • At least ten detectors, each with positive (bug detected) and negative (contract cleared) test cases
  • Counterexample output includes concrete assignments for free symbolic variables
  • Validation against real Canton contracts and known-vulnerable patterns drawn from prior audit experience

Milestone 3: CLI, Integration, Documentation, and Community Onboarding

• Estimated Delivery: 14 weeks from project start
• Focus: Polished for external use - standalone CLI, CI integration, full documentation, and a public demo on a real Canton project. Joint technical article with the Canton Foundation.
• Deliverables:
  • Published CLI artifact, installable via standard package managers
  • CI workflow template for any Canton project
  • Precision benchmark on a corpus of known-correct contracts
  • User documentation: quickstart, detector reference, FAQ
  • At least one real Canton ecosystem project has run DamlSec and reviewed the results, in coordination with the Foundation
  • Co-authored technical article with the Canton Foundation

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Acceptance Criteria

The Tech & Ops Committee will evaluate completion based on:

• Milestone 1: Repository public, CI green, and a live demo of symbolic execution on representative templates in a technical review session.
• Milestone 2: At least ten detectors present in the repository with passing tests, and a written report documenting detection results on the validation corpus, including real findings and contracts correctly cleared.
• Milestone 3: CLI artifact published and installable; co-marketing technical article published.
• All milestones: Code released under a compatible open source licence and accompanied by sufficient documentation for a Scala developer to build and run it independently.

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Funding

Total Funding Request: 250,000 CC

Project duration is under 6 months; the Canton Coin denomination is fixed for the life of the grant.

Payment Breakdown by Milestone

• Milestone 1 - Core Symbolic Engine: 80,000 CC upon committee acceptance
• Milestone 2 - Security Detectors and Counterexample Generation: 100,000 CC upon committee acceptance
• Milestone 3 - CLI, Integration, Documentation, Community Onboarding: 70,000 CC upon final release and acceptance

Post-Grant Maintenance Commitment

Dedaub commits to maintaining the DamlSec repository for two years following Milestone 3 acceptance. Maintenance includes: addressing critical issues filed against the tool (no feature requests), tracking breaking changes in the DAML SDK, tracking dependency releases, and reviewing community-contributed detectors. This commitment is independent of further grant funding.

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Co-Marketing

Upon Milestone 3 release, Dedaub will collaborate with the Foundation on:

• Announcement coordination: coordinated release announcement via Canton Foundation channels and Dedaub's security research blog
• Technical article: a detailed joint piece describing the tool's architecture, the vulnerability classes it detects, results on real Canton contracts, and guidance for DAML developers on interpreting and acting on findings
• Ecosystem promotion: presentation at a Canton Foundation technical forum or developer event; DamlSec listed in Canton's developer tooling directory
• Audit pipeline integration: Dedaub will incorporate DamlSec into its own DAML audit workflow and document the integration as a reference for other audit firms

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Motivation

Canton secures institutional financial workflows - tokenized securities, cross-bank settlement, real-world assets - with the value at risk measured in trillions of dollars. Yet the automated security tooling available to developers writing those contracts is sparse. The Canton Network Developer Experience and Tooling Survey Analysis (2026) documents this gap clearly: security and auditing tooling is among the most requested categories. The current landscape of security-focused tools confirms the gap:

Tool	Provider	Approach	Key Limitation
DAML compiler	Digital Asset	Type checking, structural authorization	Not path-sensitive; cannot reason about runtime values or cross-path logic
daml-lint	OpenZeppelin (2026)	Syntactic pattern matching on source AST (6 rules)	Purely syntactic; cannot determine whether a flagged pattern is actually reachable
daml-props	OpenZeppelin (2026)	Property-based testing / fuzzing (pure DAML)	Samples the input space; no coverage guarantee; misses rare path combinations
daml-verify	OpenZeppelin (2026)	SMT verification on manually constructed Python model	Requires manual model construction per contract; model may diverge from the actual implementation
daml-lf-verify	Digital Asset (2020)	Symbolic execution as Haskell compiler plugin	Abandoned; narrow scope (fund preservation only); unmaintained since ~2020
Manual audit	Various	Human code review	Does not scale; no systematic path enumeration; findings not reproducible

No existing tool provides automated, path-sensitive security analysis that operates directly on compiled DAML artifacts, covers all feasible execution paths, and produces concrete counterexamples. DamlSec occupies this gap. DAML-specific vulnerabilities - authorization bypass, conservation violation, privacy leaks - are quiet and subtle, which makes automated detection more valuable, not less.

DamlSec is a common-good infrastructure investment: every project deploying DAML contracts on Canton benefits from the same security properties the tool checks. It is open source, architecturally designed for community extension, and aligned with CIP-0082's explicit priorities around security and developer tooling.

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Rationale

Why the DamlSec Approach

Static analysis. Pattern-matching based static analysis catches a subset of bugs - missing guards, obvious unguarded divisions - but cannot reason about whether a problematic branch is actually reachable given the contract's invariants. It is useful for triage, and as an accelerator for more powerful symbolic reasoning. We do aim to employ some techniques from this field.

Fuzzing and property-based testing. Random-input testing is powerful for finding implementation bugs but provides no coverage guarantee: a conservation violation that only manifests on one specific allocation across paths will be missed with overwhelming probability. In our experiments, a test suite written by a modern LLM provides more code coverage than fuzzing. Symbolic execution exhausts all feasible paths up to a configurable bound, making it complementary - it proves the absence of findings within the explored depth rather than providing probabilistic assurance.

Formal proofs. Approaches that require manually constructing a symbolic model of the contract are prohibitively expensive for routine audit use and introduce the risk that the model diverges from the implementation. DamlSec works directly on the compiled artifact - no model construction, no divergence risk.

Novel integrations of symbolic execution with static analysis occupy the correct point in the tradeoff space: automated, path-sensitive, counterexample-producing, and operating on the actual compiled code.

Why Build on the Scala Toolchain

A previous attempt at DAML symbolic execution was written in Haskell as a compiler plugin and was ultimately abandoned. Maintaining a plugin against a moving compiler target, combined with the fragility of interfacing Haskell with production-grade SMT solvers, contributed to its deprecation. DamlSec is written in Scala, lives outside the compiler, takes compiled artifacts as input (a stable binary interface), and uses a Scala-native theorem prover. This is a more maintainable architecture on every dimension and specifically eliminates fragility that contributed to the prior tool's abandonment.

Why a Two-Tier SMT Stack

DamlSec is built around a clear design principle drawn from prior experience with theorem proving on smart contract bytecode: heavyweight SMT solvers are inadequate for symbolic reasoning that targets exploration of security violations. Heavyweight solver calls on the kinds of queries symbolic execution generates can cost seconds each, so even a modest call rate dominates analysis time. The right architecture is a cheap inner pre-pruning stage, with the expensive solver reserved for the small number of candidates that genuinely need it.

DamlSec realizes this principle with solvers tuned to DAML's actual workload. The inner-tier prover does aggressive simplification and fast feasibility checking on the hot loop, sub-millisecond per query. The outer-tier solver is expensive, called only on the survivors, and used to discharge security obligations and produce concrete counterexamples. This gives DamlSec the throughput of a custom-built pipeline without paying the engineering cost of building one from scratch.

Why Dedaub

Dedaub is a blockchain security firm whose research team built static analysis and symbolic execution tools for Ethereum used by tens of thousands of users. The team has published extensively on program analysis, decompilation, symbolic reasoning techniques, AI, and smart contract security at top venues (PLDI, OOPSLA, ICSE). We have directly audited DAML/Canton codebases, giving us first-hand knowledge of the vulnerability patterns DamlSec is designed to detect. We have Scala engineers on staff who understand the DAML toolchain, and a working prototype already exists. This is an execution risk we have substantially de-risked. Finally the team has been awarded millions in bug bounty rewards, for disclosing critical vulnerabilities in popular web3 protocols.