pbfuzz-dsh

PBFuzz (the CCS'26 agentic directed fuzzer) as plugins for DeepSeek Harness (DSH). Point the agent at a target repo and a bug (a CVE, a patch, a crash trace, or just file:line); it runs a directed-fuzzing loop until it produces a verified Proof-of-Vulnerability input.

Quick start

Ubuntu 22.04/24.04 x86-64, Node.js ≥ 22, Python ≥ 3.11

1. Set up DeepSeek Harness — follow the official docs to install DSH and configure your DeepSeek API key. Use the DSH version these plugins are verified against (DSH_VERSION in HARNESS_COMMIT); npm's latest can be outside their peer range.

2. Install pbfuzz from npm

npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web add @pbfuzz/dsh-pbfuzz

That is all: the package ships the Python engine, its schemas and PyYAML. Then start DSH with npx @deepseek-ai/dsh@0.2.0-rc.2 web.

Optional static analysis (C/C++ reachability; needs an LLVM 14 toolchain, see packages/dsh-kanalyzer):

npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web add @pbfuzz/dsh-kanalyzer

Manage the install with the same plugin command (add --profile NAME for a profile other than web), and restart DSH afterwards:

npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web list                              # installed plugins and versions
npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web add @pbfuzz/dsh-pbfuzz@0.1.14     # a specific release
npx @deepseek-ai/dsh@0.2.0-rc.2 plugin --profile web remove @pbfuzz/dsh-pbfuzz

To move to a newer release, add it by exact version; npm view @pbfuzz/dsh-pbfuzz version prints the newest one. Releases are listed at npmjs.com/package/@pbfuzz/dsh-pbfuzz.

From source

git clone <this-repo-url> pbfuzz-dsh && cd pbfuzz-dsh
./install.sh

That builds the plugins and the Python engine and adds the local build to DSH. Restart npx @deepseek-ai/dsh@0.2.0-rc.2 web and you're done.

Script	Does
./install.sh	build + install into DSH (profile web; --profile NAME to change)
./install.sh --with-kanalyzer	also the static-analysis plugin and its LLVM 14 toolchain (slow, uses sudo)
./build.sh	build only — no changes to your DSH install

Use

Open a session in the target repository and describe the bug in plain language — pbfuzz's skill triggers automatically:

There's a bug in src/http/modules/ngx_http_rewrite_module.c:178 (nginx). Reproduce it as a verified PoV.

The agent probes the repo, proposes a campaign (target, build command, oracle) and shows you a one-screen summary to Approve or Revise. Once approved it drives PLAN → IMPLEMENT → EXECUTE → REFLECT on its own — writing a fuzzing plan, a generator, running it, and reflecting on the result — until it reports a triggering input or gives up and explains why.

Useful commands inside a session:

Command	Does
/pbfuzz [notes]	start a new campaign, with notes as the bug report
/pbfuzz run <campaign.yaml>	headless: load a hand-written, already-confirmed campaign and start PIER with no prompts
/pbfuzz status	print the current campaign's phase and metrics
/pbfuzz selfcheck	run the environment self-check (LLVM, tracers, Python) now

Settings (tracer, budget caps, oracle defaults, which auxiliary analyses are on) live under Plugins → @pbfuzz/dsh-pbfuzz → Configure in the web UI, or under the pbfuzz entry's config: in your profile's cordis.patch.yml. Three budgets — budget.maxPierRounds, budget.campaignWallTimeMin, budget.maxConsecutiveForcedContinues — are the only brake on an unattended run; reaching one stops the campaign for real (phase: STOPPED).

Worked examples for C/C++, Python and Java targets are in examples/; headless runs on Magma benchmark bugs are in examples/magma/.

How it works

• @pbfuzz/dsh-pbfuzz — the campaign FSM (PLAN → IMPLEMENT → EXECUTE → REFLECT, "PIER"), its tools (pbfuzz_campaign, pbfuzz_plan, pbfuzz_fuzz, pbfuzz_reflect, …), the state guards that keep the model from tampering with engine-owned evidence, and the settings/dashboard UI. It runs as a native Cordis plugin — no Claude-Code-style hooks, no compatibility layer.
• @pbfuzz/dsh-kanalyzer (optional) — a general-purpose LLVM static-analysis plugin (reachability, call graphs) that dsh-pbfuzz consumes when static analysis is turned on.
• engine/ — the Python sidecar that actually runs the target: generates inputs, executes them, judges the oracle, and is the sole writer of metrics.json. It speaks JSON-RPC over stdio and never runs model-written code outside a resource-bounded sandbox.

Repository layout

packages/dsh-pbfuzz/     the pbfuzz plugin: src/, skills/, canaries/, tests/
packages/dsh-kanalyzer/  the static-analysis plugin
engine/                  the Python fuzzing sidecar (pbfuzz_engine/)
contracts/               JSON Schemas shared by both plugins and the engine (pnpm codegen)
examples/                worked targets: C/C++, Python (atheris), Java (Jazzer)
dsh-tests/               tests that boot the real DSH in-process (no mocks), always on npm's latest
docs/                    DSH upgrade playbook, verification records

Development

pnpm -r typecheck
pnpm -r test                 # vitest, both packages
pnpm run test:engine         # pytest, the Python engine
pnpm run test:dsh            # the real DSH, newest from npm, booted in-process (needs the network; no API key)
pnpm run check:clients       # settings-card ⇔ schema parity, client bundle self-checks
pnpm run codegen:check       # contracts/ generated files are up to date

pnpm run codegen regenerates the TypeScript/Python types under contracts/ after any schema change; commit the generated diff together with the schema.

DeepSeek Harness compatibility

DSH changes its plugin APIs between releases, so every push and PR runs dsh-tests/ against the newest DSH on npm (the real DSH booted in-process; nothing mocked), and a weekly workflow checks pbfuzz against it again and opens an issue (plus a fix PR, if ANTHROPIC_API_KEY is set) when it breaks. Reproduce or check by hand with scripts/compat-check.sh [version | --current]; what changed in 0.2 and how to fix the next break is in docs/dsh-upgrade.md.

License

PolyForm Noncommercial License 1.0.0

Citation

@misc{zeng2025pbfuzzagenticdirectedfuzzing,
      title={PBFuzz: Agentic Directed Fuzzing for PoV Generation},
      author={Haochen Zeng and Andrew Bao and Jiajun Cheng and Chengyu Song},
      year={2025},
      eprint={2512.04611},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2512.04611},
}