[Paper] SEAL: Symbolic Execution with Separation Logic (Competition Contribution)

Source: arXiv - 2602.05703v1

Overview

The SEAL project introduces a new static analysis tool that can automatically verify programs that manipulate unbounded linked data structures (think lists, trees, and other pointer‑heavy structures). By marrying separation logic with a modern SMT‑based solver, SEAL achieves a level of modularity and extensibility that sets it apart from earlier verification frameworks, and it performed competitively in the recent SV‑Comp “LinkedLists” category.

Key Contributions

• General‑purpose separation‑logic engine – SEAL reuses the Astral solver, which translates separation‑logic entailments into SMT queries, avoiding the need for a hand‑crafted, domain‑specific prover.
• Modular architecture – The analysis pipeline is cleanly split into front‑end (program parsing), abstract‑state representation, and back‑end (SMT solving). This makes it straightforward to plug in additional theories (e.g., arithmetic, arrays).
• Support for unbounded lists – SEAL is one of only four tools that can prove correctness of programs that allocate and manipulate lists of arbitrary length, a long‑standing challenge for static analyzers.
• Competitive competition results – In the SV‑Comp “LinkedLists” base category, SEAL matched or outperformed many established tools despite being a prototype.
• Open‑ended extensibility – The authors explicitly design SEAL to be a research platform that can be extended with new abstract domains, custom predicates, or hybrid reasoning strategies.

Methodology

1. Program Front‑end – SEAL parses C (or a subset used in the competition) and builds a control‑flow graph (CFG).
2. Abstract Memory Model – Memory states are expressed in separation logic: heap fragments are described by predicates (e.g., list(x) for a linked list starting at x). This naturally captures disjointness and sharing.
3. Symbolic Execution – The tool walks the CFG symbolically, updating the separation‑logic formula at each program point.
4. Entailment & Satisfiability via Astral
   • Each symbolic step may generate a verification condition (VC) that asks whether a current heap predicate entails a required one (e.g., after a free, the list predicate should no longer hold).
   • Astral translates these VCs into SMT‑LIB formulas, delegating the heavy lifting to state‑of‑the‑art SMT solvers (Z3, CVC5, …).
5. Result Aggregation – If all VCs are proved, the program is declared safe; otherwise, a counterexample trace is produced.

Because the heavy logical reasoning is outsourced to an SMT solver, SEAL avoids implementing its own low‑level decision procedures, which is where most of the engineering effort in traditional separation‑logic tools resides.

Results & Findings

The key takeaway is that modularity does not have to sacrifice verification power. By leveraging Astral + SMT, SEAL can handle the same class of problems as more monolithic tools, while keeping the codebase lean and adaptable.

Practical Implications

• Safer low‑level code – Developers writing systems code (e.g., kernel modules, embedded drivers) often manipulate linked structures manually. SEAL can automatically prove absence of common bugs such as null‑dereferences, memory leaks, or malformed list updates.
• Integration into CI pipelines – Because SEAL’s back‑end is an SMT solver, it can be run headless and produce machine‑readable proof certificates, making it suitable for continuous‑integration checks.
• Extensible verification platform – Companies that need domain‑specific invariants (e.g., network packet buffers, lock‑free queues) can add custom separation‑logic predicates without rewriting the core solver.
• Hybrid analysis – The architecture allows coupling with other static analyses (e.g., abstract interpretation for numeric ranges) to reason about mixed data (lists + integer counters) in a single pass.

In short, SEAL opens the door for practical, scalable verification of pointer‑intensive code without demanding deep expertise in theorem proving from the end‑user.

Limitations & Future Work

• Prototype maturity – The current implementation lacks many engineering refinements (e.g., incremental solving, parallelism) that would improve scalability on large code bases.
• Performance overhead – Translating separation‑logic entailments to SMT incurs a non‑trivial cost; for very large programs the analysis can become slower than specialized, hand‑tuned tools.
• Language coverage – SEAL focuses on a restricted C subset used in the competition; full‑C support (macros, inline assembly) remains out of scope.
• Future directions suggested by the authors include:
  • Adding domain‑specific extensions (e.g., predicates for trees, graphs).
  • Improving solver integration (caching, incremental queries).
  • Exploring combination with other theories such as arithmetic or bit‑vectors to verify more complex invariants.

Overall, SEAL demonstrates that a clean separation‑logic + SMT architecture can be both powerful and extensible, and with further engineering effort it could become a mainstream tool for developers who need rigorous guarantees about their pointer‑heavy code.

Authors

• Tomáš Brablec
• Tomáš Dacík
• Tomáš Vojnar

Paper Information

• arXiv ID: 2602.05703v1
• Categories: cs.SE
• Published: February 5, 2026
• PDF: Download PDF