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Palisade

A linter for LLM security. Palisade statically detects prompt-injection vulnerabilities in Python codebases — untrusted input flowing through an LLM into a dangerous sink — in CI, before they ship.

untrusted input  →  LLM  →  exec / shell / raw SQL   (no sanitizer)   ⇒  finding

No API key. No signup. No network calls. Pure static analysis.

uvx palisade-sec scan .
HIGH  app.py:31  [PI-EXEC] Prompt injection reaching code execution
  ↳ source:  question = request.json["question"]        (app.py:31)
  ↳ llm:     resp = client.chat.completions.create(     (app.py:32)
  ↳ sink:    exec(code)                                 (app.py:40)
  No sanitizer on path.  Confidence: HIGH
  Attack: crafted input makes the model emit Python that executes on your server.
  Fix:    never exec model output; sandbox + strict allowlist (denylists are bypassable).
  Refs:   CVE-2024-12366 (PandasAI); CVE-2025-3248 (Langflow, CISA KEV)

Why

This exact pattern is behind real, exploited CVEs: Langflow (CVE-2025-3248, on CISA KEV, exploited in the wild), PandasAI (CVE-2024-12366, CVSS 9.8), Vanna.ai (CVE-2024-5565), LangChain PAL/LLMMath chains (CVE-2023-36258, CVE-2023-29374). Almost nobody defends it at the code level: existing tools are runtime proxies (paid, in the traffic path) or guardrail libraries you have to know to wire in. Palisade is the missing piece — free, static, LLM-dataflow-aware, and CI-native, like ruff or semgrep but for the OWASP LLM Top-10 #1 risk.

What it detects (v1)

Rule Path Real-world precedent
PI-EXEC input → LLM → exec / eval / compile / PythonREPL PandasAI, Langflow, LangChain PAL
PI-SHELL input → LLM → os.system / subprocess(shell=True) Open Interpreter (by design)
PI-SQL input → LLM → raw non-parameterized SQL Vanna.ai

Palisade runs taint analysis, not grep: it only reports a complete source → LLM → sink data-flow path with no sanitizer in between.

  • Constant developer prompt → LLM → exec? Silent — no untrusted source.
  • subprocess.run([...]) with an arg list? Silent — safe sink shape.
  • Parameterized cursor.execute(q, params)? Silent.
  • Allowlist / pydantic validation on the path? Silent — sanitized.
  • Denylist or human-confirmation gate? Flagged MED "risky" — real CVEs were exploited despite exactly those defenses. That is deliberate.
  • A "sanitizer" in name only — a project function matching sanitize/ validate whose body never actually validates? Flagged MED "unverified sanitizer" — Vanna's cosmetic _sanitize_plotly_code shipped CVE-2024-5565 straight through such a function.

Install & run

# one-shot, no install
uvx palisade-sec scan path/to/project

# or
pipx run palisade-sec scan .

# or as a dev dependency
uv add --dev palisade-sec

Useful flags:

palisade-sec scan . --all          # also show MED/LOW findings
palisade-sec scan . --json         # stable machine-readable output
palisade-sec scan . --report       # write palisade-report.md
palisade-sec scan . --rules ./my-rules   # add your own YAML rules
palisade-sec scan . --assume-params-untrusted   # library mode, see below

Scanning libraries

Apps read untrusted input from request.* / input() / sys.argv. A library has no visible caller — its public parameters ARE the untrusted world (Vanna's ask(question), CVE-2024-5565). Library mode treats the parameters of public (non-underscore) functions as untrusted sources:

palisade-sec scan path/to/library --assume-params-untrusted

If the library routes LLM calls through its own wrapper method, add the wrapper to a custom rule's llm_signatures (e.g. "*.submit_prompt") — see the rules guide.

CI

Gate pull requests on new findings only — adopt Palisade on an imperfect codebase without a wall of pre-existing failures:

palisade-sec baseline .                 # once; commit .palisade/baseline.json
palisade-sec scan . --ci --baseline .palisade/baseline.json

--ci exits non-zero only if a new HIGH finding appears. Fingerprints are line-number independent, so refactors don't churn the baseline.

GitHub Actions:

- uses: astral-sh/setup-uv@v5
- run: uvx palisade-sec scan . --ci --baseline .palisade/baseline.json

Configuration

pyproject.toml:

[tool.palisade]
paths_ignore = ["migrations/*", "sandbox/*"]
include_tests = false   # tests/** and conftest.py are skipped by default
max_hops = 3            # inter-procedural depth bound
assume_params_untrusted = false   # library mode (see "Scanning libraries")

Or the same keys in .palisade.toml.

Custom rules

Rules are plain YAML validated by a pydantic schema — sources, LLM call signatures, sinks, sanitizers, partial defenses. Adding coverage for a new framework is a small PR with no engine changes. See src/palisade_sec/rules/README.md for the 5-minute guide.

Architecture

source ──▶ language frontend (Python: stdlib ast) ──▶ normalized taint IR
                                                        │
                              language-agnostic engine ─┤ taint propagation,
                              sanitizer resolution, confidence scoring
                                                        │
             YAML rules ──▶ findings ──▶ baseline diff ──▶ terminal / json / md

The frontend/IR split is the scalability story: JS/TS/Go land later as new frontends (tree-sitter) with zero engine changes.

Safety of the tool itself

  • Palisade never executes, imports, or evaluates scanned code — it only parses source text with ast.parse.
  • scan makes no network calls and needs no API key or account.
  • No telemetry. Nothing leaves your machine.

An honest note on scope

Palisade is one layer of defense against one class of vulnerability. A clean scan means no detected injection-to-sink path — it does not mean your application is secure. Keep your runtime guardrails, permissions boundaries, and sandboxes; Palisade complements them, before merge.

License

MIT

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