fenceline

Zero-day security scanner for Python codebases. Generic, pip-install-anywhere tool — pip install fenceline && fenceline scans whatever's under the current directory, no project-specific setup required.

Installation

uv add fenceline

Maps every finding to a CWE from the 2025 CWE Top 25, OWASP Top 10:2025, and known Python zero-day exploit patterns (pickle bypass CVE-2026-56315, PyYAML shadow vulnerability CVE-2026-24009, LangChain SSTI CVE-2025-68664, dependency confusion CVE-2025-61774, and more — see the CWE reference printed at the end of every text-mode report). 56 checks total: most are AST-based (won't false-positive on a dangerous call mentioned in a docstring or comment); the rest are line-regex checks for surface-syntax patterns that don't need full parsing.

Usage

uv run fenceline
uv run fenceline --json > report.json
uv run fenceline -q
uv run fenceline --fail-on critical
uv run fenceline --package my-lib=my-lib/src/my_lib
uv run fenceline --packages my-lib other-lib

Exit codes: 0 (no findings at or above --fail-on), 1 (one or more).

Options

What gets scanned by default

A bare fenceline invocation (no --package) auto-discovers packages from the current directory: every immediate subdirectory containing at least one .py file anywhere in its subtree becomes its own named package, skipping noise directories (.venv, .git, __pycache__, node_modules, build, dist, *.egg-info, and similar caches — also excluded within a scanned subtree, not just at the top level). Loose .py files sitting directly in the current directory (outside any subdirectory) are scanned too, grouped into a package named after the directory itself.

Pass --package NAME=PATH (repeatable) to scan an explicit set of directories instead — this replaces auto-discovery entirely rather than adding to it. Use --packages NAME [NAME ...] afterwards to narrow down to a subset of whichever registry was resolved.

Severity vs. confidence

Every finding carries two independent ratings, the same distinction Bandit makes: severity is how bad it would be if the finding is real; confidence is how sure the check is that it is real. An AST-based check that matched a real Call node is HIGH confidence; a line-regex check that can't fully rule out a docstring or string-literal mention is MEDIUM; a handful of inherently heuristic checks (timing-attack keyword proximity, ReDoS backtracking-risk judgment, debug=True pattern matching) are LOW. Use --confidence-min medium to cut noise from the fuzzier checks without raising your severity bar.

Baselining an existing codebase

Adopting fenceline on a codebase with existing findings doesn't mean fixing all of them before CI can pass:

uv run fenceline --write-baseline fenceline-baseline.json   # snapshot today's findings
uv run fenceline --baseline fenceline-baseline.json          # only new findings fail CI

A finding is matched against the baseline by (cwe_id, file, code_snippet), not line number, so it keeps matching across unrelated edits that shift line numbers elsewhere in the file. Re-run --write-baseline periodically (or whenever a baselined finding is deliberately fixed) to keep it current.

Suppressing a specific finding

# nosec (bare) suppresses every finding on that line; # nosec CWE-502 suppresses only that CWE, leaving any other finding on the same line intact — the same convention Bandit uses, with CWE IDs instead of Bandit's own B-numbers:

pickle.loads(data)  # nosec CWE-502 -- trusted internal cache, not user input

Extending fenceline with your own checks

Built-in checks aren't dynamically discovered — they're a fixed list. Your own checks are, via a fenceline.checks entry point in your own pyproject.toml:

[project.entry-points."fenceline.checks"]
"My Custom Check (CWE-000)" = "my_package.checks:my_check_function"

The entry point's own name becomes the check's display name; the object it points at must be a function with the same (path, lines, tree) -> list[Finding] signature every built-in check has. A plugin that fails to import is skipped with a warning rather than crashing the whole scan.

Design

• fenceline.models — the Finding dataclass, severity ordering, and confidence ordering.
• fenceline.config — workspace-root discovery and the default package registry.
• fenceline.ast_helpers — shared AST/text-matching helpers used across checks.
• fenceline.scanner — file discovery and reading.
• fenceline.checks — the built-in check registry, plus fenceline.checks entry-point discovery for third-party checks.
• fenceline.checks.ast_checks — checks that walk the parsed AST (won't match a dangerous call mentioned in a docstring or string literal).
• fenceline.checks.text_checks — checks that scan raw source lines for surface-syntax patterns.
• fenceline.checks.manifest_checks — checks over dependency manifests (pyproject.toml, requirements.txt, etc.), not Python source.
• fenceline.suppression — # nosec inline-suppression parsing.
• fenceline.baseline — baseline snapshot/diff for adopting fenceline on an existing codebase.
• fenceline.reporting — text/JSON report rendering.
• fenceline.cli — argument parsing and the scan loop.