sabba

Two real cJSON vulnerabilities, reproduced and proved by running them: a stack exhaustion (CWE-674, fixed upstream in 2017) and a heap over-read in parse_object (CWE-125, fixed in 2024). Both came out of variant analysis from the upstream fix commits, so they are reproductions of known bugs and not new discoveries. What Sabba contributes is the proof: each write-up in docs/scans carries the exact input and a bundle you can re-run on your own machine to watch AddressSanitizer fire. Novel zero-day discovery is a later phase and is not something this repository claims yet.

That is the whole design. Most tools that use a language model ask it "is this function vulnerable?" That is close to a coin flip, even for large models, and unverified guesses bury maintainers in false positives. Sabba takes the opposite stance: a model proposes candidates, but an execution oracle runs an exploit and decides whether a security property actually broke. Nothing is reported unless the exploit reproduces. A finding is not a score, it is a re-runnable proof.

Use it from any coding agent (MCP)

Sabba runs as an MCP server, so Claude Code, Codex, OpenCode, Cursor, and Hermes can call it. For Codex CLI, add it to ~/.codex/config.toml:

[mcp_servers.sabba]
command = "sabba"
args = ["mcp"]

For Claude Code:

claude mcp add sabba -- sabba mcp        # after installing; see Install below

Fourteen tools, most token-free: verify_change (prove a change works in any of 16 languages: a new test fails on the base and passes on the head, via the bundled Magga engine) and prove (the same differential, run natively for C/C++/EVM), verify / solve / hunt / scan (find and prove bugs), security_scan (vet a skill by running it under observation), rank, run_sandboxed, and kali_run (drive nmap / nuclei / ffuf / sqlmap and the rest, scope-enforced and sandboxed). Install the security command templates with sabba templates install. Full catalog and per-client configs in docs/AGENT_INTEGRATION.md.

Correctness and security in one server. verify_change proves the change does what it claims; prove / hunt / scan prove it added no new bug. The change-verification engine is Magga, vendored as a submodule under magga/ and driven through npx, so both halves ship as one tool.

Related MCP server: aegis

What SABBA can do

Find a real bug and hand you the proof, not a hunch. Every finding ships as a bundle: the input that triggers it, the target, the command that reproduces it, and the sanitizer output it produced. You do not have to trust the report, you can re-run it. The cJSON bugs above are two of these bundles.

Work across languages and across chains, with one rule. The oracle started on C and C++ memory safety and generalized into a registry of provers, one per runtime and vulnerability class. Every prover obeys the same contract: a finding is minted only from a verdict that a real, security-relevant crash happened inside the target.

Refuse to be fooled, even by a hostile harness. When a model writes the fuzz harness, a hostile target could try to steer it into faking a crash. Sabba's fuzzing provers are harness-untrusted: the fuzzer only discovers a candidate input, then a Sabba-owned reproducer re-runs it and reads the verdict from channels the harness cannot forge (a real exception's structured stack, or the parent's own measurement of a killed child). It reads no stdout, no artifact file, no magic phrase. The full model is in docs/PROVER_SOUNDNESS.md.

Prefer soundness over coverage, and say so. Where a crash cannot be soundly pinned to the target (a hang or an out-of-memory that could just as easily be the harness spinning or pre-filling the heap), Sabba surfaces it as an unverified candidate for a human, but never mints it as a finding. It would rather miss a bug than report one that did not happen.

Meet you where you work. One command, several surfaces: a scriptable CLI (verify, solve, hunt) and an interactive REPL (pictured above) that streams the model, runs tools, and renders each proof as a card. Running sabba with no arguments opens the REPL.

Run it locally, and let it learn where to look

The oracle and provers never needed a model, and the model-driven parts can run on your own machine too. Point the reasoning at a local, OpenAI-compatible endpoint with SABBA_LLM_BACKEND=local, and train a small CPU risk ranker so retrieval looks at the risky functions first:

sabba mltrain          # trains a risk ranker (TF-IDF + logistic), saved to ~/.sabba

A three-tier cascade keeps work cheap: Reflex (no model: the ranker, Z3, the oracle), Resident (the local model), and Teacher (a frontier model) only for the hard cases. The verdict rule holds across tiers, so a cheaper tier costs coverage, never soundness. See docs/LOCAL_ML.md.

Why it is different

                 model / z3 / retrieval  ->  candidate input
                                                   |
                                                   v
                     +---------------------------------------+
                     |   execution oracle  /  prover         |
                     |   compile, run the exploit, measure   |
                     +---------------------------------------+
                                    |            |
                              reproduces     does not
                                    |            |
                                  FINDING     dropped

The oracle is the one gate. Whether a candidate came from the Z3 synthesizer or from the model, it is compiled and run before anything is reported. Z3 proposes an input, the oracle decides. The model proposes an input, the oracle decides. The same discipline carries to every domain in the table above: on an EVM fork the chain measures the attacker's profit, not the model, so the model cannot grade its own work.

Install

pip install sabba          # or: pipx install sabba / uvx sabba mcp

Then run sabba doctor to see what the toolchain can prove on this machine. verify_change shells out to the Magga engine over npx, so it needs Node on your PATH but no extra install step.

To work on Sabba itself, clone it with the submodule and use the installer, which sets up an isolated environment under ~/.sabba and puts a sabba command on your PATH:

git clone --recurse-submodules https://github.com/8NobleTruths/sabba.git
cd sabba
./install.sh

Update later with sabba update, remove with sabba uninstall.

Provers use the toolchain of the domain you target: clang with AddressSanitizer for C and C++, Foundry for EVM, and atheris, go, Jazzer, or Jazzer.js for the managed languages. sabba doctor reports what is present.

Quick start

sabba                                     # opens the REPL; type /setup for guided first-run setup

# no model needed, prove a known target:
sabba verify cwe121_stack_overflow
sabba solve  cwe121_stack_overflow

Those two names are demo targets that ship inside the package, so an installed Sabba can prove a real bug on the first command, with no clone, no model, and no API key. Point the same commands at a directory of your own holding a target.json to work on your code instead.

First run opens a guided setup: /setup shows a checklist, and each step explains why it is worth doing, what happens if you skip it, and what happens when you do it. /local-llm-config detects your CPU and RAM, recommends a Qwen2.5-Coder size, and pulls it with Ollama so the model runs on your machine; /add-model-key uses a cloud model instead; /ml-config trains the risk ranker. You can select any command from the / menu. /solve and /verify prove bugs with no model at all, so they work before any setup.

Bring in a model through OpenRouter (or any OpenAI-compatible endpoint) to hunt fresh code:

export SABBA_LLM_BACKEND=openrouter
export OPENROUTER_API_KEY=...             # from openrouter.ai/keys
sabba hunt cwe122_heap_overflow --model qwen/qwen-2.5-coder-32b-instruct

Keys are read from the environment, never stored in the repo, and a pre-commit hook blocks anything that looks like a credential (see CONTRIBUTING.md).

How it works, in more depth

• docs/SABBA_AGENT_DESIGN.md - the C and C++ bug-finder: the oracle, retrieval, the Z3 synthesizer, and the reasoning agent.

• docs/PROVERS_MULTI_DOMAIN_DESIGN.md - how the oracle generalizes into the prover registry, including Web3 and Solidity.

• docs/PROVER_SOUNDNESS.md - the harness-untrusted verification model that makes the fuzzing provers sound against an adversarial harness.

• docs/WATER_LAYER_DESIGN.md - the next layer: an agent that keeps its skills as runnable code, runs without a frontier model, and can be rebuilt from a seed. Provers are the skills it accumulates.

Status

The native oracle, retrieval, Z3 synthesis, the reasoning agent, and the full prover registry across C/C++, Solidity/EVM, Python, Go, Java, and Node run today, each with live proofs. The Water Layer and a broader symbolic-execution pass are next.

License

Apache-2.0. See LICENSE. The framework is open source. Trained model weights and datasets are developed separately and are not part of this repository.