ai-agent-shield

agent-shield

Defensive overlay for AI agents. An 8-layer security stack that sits between your agent and the outside world — inspecting, sanitizing, auditing, and blocking risky actions before they reach the host.

A PDuk Brainworks project. Sibling to the Ultimate Memory Stack. Apache-2.0.

---

What it does

agent-shield is a layered defensive overlay for AI agents. It intercepts what your agent is about to do — run a command or write a file — and applies a tiered RED / YELLOW / GREEN decision policy before letting it through. (Network egress — fetching a URL — is the roadmap Layer 5, not yet shipped.)

v0.2.0 ships six of the eight layers — Layers 1, 2, 3, 4, 6, and 7 (see The 8 layers and Project status):

• Layer 4 — Runtime Hooks is the headline runtime surface. It runs on a harness-agnostic architecture (one neutral decision core, per-harness adapters) with two functional adapters shipping today: Claude Code (via PreToolUse hook; CI-verified) and OpenClaw (via before_tool_call; live but enforcement requires a recent gateway — see docs/adapter_status.md). Additional harnesses (Codex, Gemini, Copilot, etc.) are roadmap.
  • bash_guard — inspects bash commands before execution
  • write_guard — inspects write / edit targets before they touch disk
• Layers 1, 2, 3, 6, 7 — skill_vetting, sanitize (incl. F-001 harness-tag-spoofing detection), structured_output, audit, and config — ship as importable library modules.

Layers 0 (operational/automation) and 5 (network egress) are in development.

---

See it in action

The Layer 4 hook's actual decisions and reason strings, rendered for readability — the live hook emits them as JSON. A safe command prints nothing and exits 0; only ask and deny emit output. Run the three checks yourself: demo/demo.sh.

---

Quick start

Prerequisites: Python 3.11+ (standard library only — zero runtime dependencies). On Windows, run the shell commands in Git Bash, WSL, or PowerShell (adapt as needed); the guards themselves are pure Python.

Install (pick your door):

# From PyPI (recommended):
pip install ai-agent-shield

# From the repo (latest main, unreleased changes):
#   pip install git+https://github.com/esoteric1entity/agent-shield.git

Or tell your agent: clone the repo and say "install this — read INSTALL_AGENT.md and follow it." The agent walks a documented, consent-gated flow (it never touches your settings without showing you the diff).

Use as a library:

from agent_shield import bash_guard, write_guard

result = bash_guard.check_command("rm -rf /")
print(result.decision)  # "deny"
print(result.reason)    # "Destructive rm -rf targeting root directory"

result = write_guard.check_path("/foo/.claude/settings.json")
print(result.decision)  # "deny"

Use as a Claude Code PreToolUse hook:

echo '{"tool_input":{"command":"rm -rf /"}}' | python -m agent_shield.bash_guard
# {"hookSpecificOutput": {"hookEventName": "PreToolUse",
#                          "permissionDecision": "deny",
#                          "permissionDecisionReason": "..."}}

Reading the output: a safe command prints nothing and exits 0 — empty stdout means allow. Only ask and deny emit JSON.

Wire it into ~/.claude/settings.json (the module form below — python -m agent_shield.bash_guard — is the most portable; the agent-shield-bash-guard / agent-shield-write-guard console scripts installed with the package also work when the package's scripts directory is on PATH):

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [{"type": "command", "command": "python -m agent_shield.bash_guard", "timeout": 5}]
      },
      {
        "matcher": "Write|Edit|MultiEdit",
        "hooks": [{"type": "command", "command": "python -m agent_shield.write_guard", "timeout": 5}]
      }
    ]
  }
}

Use it with OpenClaw (or another harness): the same neutral decision core backs every harness. Wire the OpenClaw before_tool_call hook to the agent-shield-openclaw-guard console script (or python -m agent_shield.adapters.openclaw) — it reads the tool event as JSON on stdin and returns the block / ask / allow decision.

For OpenClaw, the package ships a ready-to-install companion plugin directory inside agent_shield/adapters/openclaw_plugin/. The exact install command and gateway version requirement are in docs/adapter_status.md. If you installed an earlier alpha that used a bare export const hooks = {...} registration, migrate to the current plugin-SDK shape; the uninstall and migration steps are in docs/MIGRATION.md.

The library API (bash_guard.check_command(), write_guard.check_path()) is identical across harnesses.

---

The 8 layers

---

Layer 4 — Runtime Hooks (shipping)

Tier model

Every check returns a GuardResult with one of three decisions:

bash_guard.check_command(cmd) — patterns

• 24 RED patterns: destructive filesystem ops — rm -rf against root / home / cwd targets, including quoted (rm -rf "/"), split-flag (rm -r -f /), and bundled / reordered / intervening-flag (rm -rfv /, rm -fvr /, rm -rf -- /) forms, --no-preserve-root, mkfs.*, format X: / wipefs, dd onto block devices, fork bombs; remote-code execution — pipe-to-shell (curl … | bash), pipe-to-source, decode-and-execute (base64 -d | sh), process substitution (bash <(curl …)), encoded PowerShell; credential exfiltration — $VAR / ${VAR} secrets in network commands, uploading secret files (curl -d @id_rsa), piping secret files to network tools.
• 12 YELLOW patterns: recursive force-deletes off-root (rm -rf, split-flag form, Windows del /s, Remove-Item -Recurse -Force, shred); network uploads; destructive git operations; package installs; chmod 777; Windows registry edits; service/process control; disabling the agent-shield runtime guard (agent-shield-plugin disable, python -m agent_shield.plugin_cli disable, and shell-wrapped forms).
• GREEN is not a pattern list — it is the default: anything not matched by a RED or YELLOW pattern passes silently. There is no allowlist.

write_guard.check_path(file_path) — patterns

• 6 RED patterns: .claude/settings.json + settings.local.json (hook/permission configs); agent_shield/* guard modules + legacy hooks/scripts/*-guard.sh (self-protect); SSH private keys (~/.ssh/id_* — unambiguously secret); .openclaw/.env (agent API credentials).
• 12 YELLOW patterns: agent-shield policy config (agent-shield.toml, ~/.agent-shield/config.toml — Layer 7; editing changes the agent's own security policy, but the file is user-edited so ask, not deny); agent templates; orchestration rules; other hook scripts; memory files; .env* files; shell startup files (.bashrc, .zshrc, profiles — persistence vector); credential-named files (secrets.json, tokens.yaml, …); *.pem / *.key (ask, not deny — content-blind: a .pem may be a public cert and .key is also Apple Keynote's document type, so confirm rather than hard-block); CLAUDE.md; sync protocol files.
• GREEN is the default — everything else passes silently.

Path matching is normalization-hardened: backslashes, case, NTFS alternate-data-stream suffixes (file.py::$DATA), and trailing spaces/dots are normalized away before patterns run, so Windows path-disguise tricks can't slip past the $-anchored rules.

Full pattern lists live in agent_shield/bash_guard.py and agent_shield/write_guard.py. Each pattern carries an inline comment explaining the threat model it addresses. The counts above are verified by automated tests — if code and README drift, the release is blocked.

---

Layer 1 — Skill / Tool Vetting (shipping)

Vet a skill / MCP server / hook / package before you install it. skill_vetting does a static, read-only scan (it never executes, imports, writes, or network-fetches the target), scores it 0–10, and returns a 3-tier verdict:

from agent_shield import skill_vetting
r = skill_vetting.vet_path("/path/to/some-skill")
r.tier      # "approved" | "review" | "rejected"
r.findings  # list of Finding(category, severity, file, line, snippet, why)

python -m agent_shield.skill_vetting /path/to/some-skill            # exit 0/1/2 = approved/review/rejected
python -m agent_shield.skill_vetting /path/to/some-skill --format json
# the installed console script works too:  agent-shield-vet /path/to/some-skill

Threat categories (code files scanned line-by-line; instruction files scanned for injection; dependency manifests checked for typosquats): ENV_BULK, ENV_SCRAPE, CRED_PATH, FS_DANGER, PIPE_TO_SHELL, PERSIST, NET_EXFIL, CRYPTO_MINE, PROMPT_INJECTION, TYPOSQUAT. The category list is verified by automated tests — drift between code and docs is detected at release time. This is a static heuristic gate, not a sandbox — see Bypasses and limitations.

---

Layer 2 — Input Sanitization (shipping)

agent_shield.sanitize sanitizes untrusted incoming content — web fetches, tool/MCP output, user input, agent-to-agent handoffs — before it reaches the model. It strips invisible/control characters (destructive, safe) and detects and flags injection markers and suspicious encodings (non-destructive by default).

Layer 2 (sanitize) detects and flags injection markers and encodings — including forged harness framing tags (<system-reminder>, <system>, <assistant>, <user>, <instructions>) as of this release. It detects and flags these — it does not block or intercept them at the model layer: novel phrasings and creative encodings are unbounded, and semantic injection at the model layer is outside what a guard can stop. agent-shield's defense is detection, action-boundary prevention (Layer 4 blocks dangerous outcomes), cost-raising (nonce wrappers), and evidence (Layer 6 tamper-evident audit). It does not prevent determined semantic injection. For untrusted fetched content, see examples/fetch-wrap.example.py.

Harness-tag spoofing class (F-001, detected as of this release): On 2026-05-12 a class of injection was observed where attacker-controlled content attempted to harness spoof by forging structural framing tags to impersonate harness-level context boundaries. Layer 2 now detects and flags this class. No working payload is published here — see SECURITY.md for the disclosure posture.

from agent_shield import sanitize

rep = sanitize.sanitize(untrusted, source="web")
rep.text            # cleaned text (invisible/zero-width, BIDI + directional marks, tag/control chars stripped; NFKC-normalized)
rep.findings        # SanitizeFinding(sublayer, kind, span, snippet, why) — markers/encodings flagged
rep.stripped_count

clean = sanitize.clean(untrusted)           # just the cleaned string

# nonce-delimited typed wrappers (each cleans first, then wraps):
sanitize.wrap_web(content, url="https://example.com/page")
sanitize.wrap_user_input(content)
sanitize.wrap_agent_output(content, agent="planner")
sanitize.wrap_tool_output(content, tool="grep")

• Four sub-layers: structural (strip zero-width invisibles incl. WORD JOINER / invisible-math / interlinear anchors, BIDI overrides/isolates and directional marks (LRM/RLM/ALM), tag / control chars + lone surrogates; ZWJ/ZWNJ preserved), content (flag instruction-injection & jailbreak markers; opt-in strict=True neutralizes them), encoding (NFKC-normalize, then detect — never decode — base64/base64url blobs and mixed-script homoglyph tokens), context (nonce-delimited typed wrappers).
• The nonce-delimiter contract. The wrapper guarantees content cannot break out of or forge its tag (a fresh 128-bit nonce in both open and close tag is the mechanism). It does not by itself make the model obey the boundary — the consuming prompt must be instructed to honor the delimiters (and to trust only the outermost block whose nonce you issued). The wrapping only helps if that instruction is present.

Full sub-layer reference, kind taxonomy, the nonce contract, strict mode, and the bypasses & limitations: docs/SANITIZATION.md.

---

Layer 3 — Structured Output (shipping)

agent_shield.structured_output constrains agent / tool output to a declared structure and rejects what doesn't conform — reducing the blast radius of "make the agent emit free-form X." It enforces shape, not intent: a well-formed object whose values are malicious still passes, so this is one layer of defense-in-depth, not a prompt-injection blocker. It never executes, evals, or decodes the validated payload.

from agent_shield import structured_output as so

schema = so.Schema({"action": str, "target": str, "args": list, "dry_run": (bool, False)})
result = so.enforce(output, schema)          # output: a JSON string or a dict
result.ok                                     # bool
result.value                                  # validated dict (defaults filled) when ok, else None
result.errors                                 # path-qualified: "$.args[2].name: expected string, got integer"

# JSON-discipline helpers (reject "valid JSON wrapped in prose", a common injection tell):
so.expect_json(text)      # accept ONLY a single bare JSON object
so.extract_json(text)     # pull the first JSON object out of surrounding text

• Type matching is by exact identity (not isinstance): an int field rejects True/False (bool is not int), a bool field rejects 0/1; int widens to float (JSON has no 1 vs 1.0); NaN/Infinity are rejected. Supports nested Schema, list[T], dict[str, T], Union/Optional, Literal, (type, default) optionals, and Field(...) constraints (length / numeric range / regex / choices).
• Collect-all, deterministic errors in schema-declared order; strict rejects unexpected keys, lenient drops them; value is a fresh dict and enforce never mutates its input.
• Canary tokens and pydantic interop are deferred to future releases. v0.2.0 ships a stdlib-only validator.

Full DSL grammar, the type rules, strict/lenient + default-fill, the JSON-discipline helpers, and bypasses & limitations: docs/STRUCTURED_OUTPUT.md.

---

Layer 6 — Structured Audit (shipping)

agent_shield.audit is an append-only, tamper-evident forensic log: one JSON object per line, each hash-chained to the previous. It records who did what to what, with what outcome — and any later edit, insertion, deletion, or reorder of the file is detectable.

from agent_shield import audit

log = audit.AuditLog("audit.jsonl")
log.record(action="bash_guard.check", target="rm -rf /", outcome="deny")
log.record_write(target="/etc/hosts", outcome="allow",
                 content_before=b"...", content_after=b"...")  # adds SHA-256 content hashes
log.verify().ok          # True until the file is tampered with

python -m agent_shield.audit --verify audit.jsonl
#   exit 0 = chain intact · 1 = tamper detected · 2 = missing/unreadable

• Compliance presets — general (9-field rows, 90-day retention, fail-open) and healthcare/biotech (uniform 11-field rows, 365-day retention, fail-closed: "no action without an audit record").
• Fail-open by default — a logging failure prints a warning and returns None; it never blocks the operation being audited (use fail_mode="closed" to invert that).

Tamper-evidence vs tamper-resistance (and "never phones home")

The local hash-chain is tamper-evident, not tamper-proof: an attacker with write access can rewrite the whole file from genesis into a clean chain that still verifies. To get tamper-resistant integrity, anchor the chain head to a target the attacker can't reach:

log = audit.AuditLog("audit.jsonl",
                     anchor=audit.Anchor(local_path="/mnt/worm/anchor.jsonl"))

The anchor periodically (every N entries or T minutes — never per-event) copies a minimal receipt — {seq, entry_hash, ts}, no PII — to its target, fail-open: a failing shipper never blocks the audited write. Resistance holds only when that target is genuinely independent — a separate / write-once volume or another host; an anchor on the same volume as the log is not independent and stays tamper-evident only. The log file is written 0600 and is a forensic record, not a live guard. Retention/rotation enforcement is not automated in this release.

agent-shield never phones home. The shipped package makes no outbound network calls — its only socket use is a local gethostname() for the audit machine field, and the built-in anchor writes to a local path only. To anchor off-box, you bring your own shipper (a callable that transmits the receipt however you choose); the egress is your code and your policy. There is no url/endpoint parameter, and the no-network guarantee is checked by a best-effort AST lint in the test suite (not a sandbox). Your shipper runs synchronously, in-process and cannot be timed out in v0.2.0 — keep it fast, or do slow/network work asynchronously yourself. A ready-to-copy recipe and a full risk guide live in examples/remote_anchor_shipper.py and docs/REMOTE_ANCHORING.md.

Full field reference, verification procedure, and limits: docs/AUDIT_SCHEMA.md.

---

Layer 7 — Configuration (shipping)

agent_shield.config is the cross-layer spine: declare a compliance posture once and the layers read their slice. It's policy, not a trust boundary — config carries paths and policy, never secrets, and cannot weaken a built-in guard.

from agent_shield import config, audit

cfg = config.load()                 # search-path + defaults; NEVER raises
cfg.compliance                      # "general" | "healthcare" | "biotech"
cfg.audit.path                      # str (a leading ~ is expanded)
cfg.sanitize.strict                 # bool (preset-derived; true for healthcare/biotech)
cfg.structured_output.mode          # "strict" | "lenient"

log = audit.AuditLog(path=cfg.audit.path, preset=cfg.compliance)   # opt-in wiring

• Never-crash. A missing / malformed / mistyped / oversized / unknown-preset config degrades to built-in defaults with a surfaced UserWarning — a layer always runs with zero config.
• Preset parity. Compliance presets mirror audit.PRESETS exactly (general/healthcare/biotech), single-sourced — an unknown/typo'd preset can never reach AuditLog. There is no enterprise preset in v0.2.0; it is planned for a future release.
• Precedence: built-in defaults < config file < environment < explicit kwargs. The file is found by search (first existing wins): $AGENT_SHIELD_CONFIG → ./agent-shield.toml → ~/.agent-shield/config.toml. The config file is a write_guard YELLOW target. TOML only (stdlib tomllib); YAML is rejected (dependency + yaml.load footgun).
• Opt-in wiring in v0.2.0 — the guards don't auto-load config (no hot-path I/O); callers pass slices. Pattern add-ons (extra_red/extra_yellow) are deferred to a post-v0.2.0 release.

Schema, every key, the presets, search-path + precedence, env vars, and bypasses & limitations: docs/CONFIGURATION.md.

---

Library API

from agent_shield import bash_guard, write_guard, GuardResult

result: GuardResult = bash_guard.check_command("rm -rf /")
# result.decision: "allow" | "ask" | "deny"
# result.reason:   str — one-line explanation (empty string for "allow")
# result.to_hook_json(): dict | None — Claude Code PreToolUse JSON (None for "allow")

Tiers map 1:1 to decisions — RED→deny, YELLOW→ask, GREEN→allow. The tier is the conceptual model; the API exposes decision.

The package has zero runtime dependencies. Only the Python ≥3.11 standard library is required.

---

CLI / hook API

The CLI reads {"tool_input": {"command": "..."}} (for bash_guard) or {"tool_input": {"file_path": "..."}} (for write_guard) from stdin. For ask / deny it prints a Claude Code-compatible JSON to stdout; for allow it prints nothing (a silent pass, matching the bash sources). Exit code is always 0 — decisions are conveyed via permissionDecision, not exit status; this contract is enforced by a top-level catch-all and pinned by tests/test_cli.py against malformed, empty, BOM-prefixed, and UTF-16 stdin. This means agent_shield can be wired into a PreToolUse hook chain without bash glue.

Input that cannot be parsed at all cannot be evaluated and is allowed — see Bypasses and limitations for why, and what that means for you.

For deployments without Python, the tests/bash-guard.sh and tests/write-guard.sh sources are decision-equivalent (verified to produce equivalent decisions; human-readable reason strings may differ in wording) and can be used directly. These sources are not installed by pip install ai-agent-shield; copy them manually from the repository if you need them. When the bash guards find no working Python interpreter, RED checks additionally scan the raw hook JSON — degraded parsing fails closed for the dangerous tier.

---

Tests

# Full suite — guard logic, CLI, security regressions, doc-claims, AND the
# Python<->bash subprocess parity cases. Safe to run anywhere (incl. Windows):
# the parity cases auto-resolve a POSIX bash (Git-Bash / Cygwin / native) and
# skip cleanly if none is available — they never hang.
python -m pytest tests/

# Python-side only (skips the bash-subprocess parity cases)
python -m pytest tests/ -k "not sh_equivalence and not sh_runs"

# Bash-only (validates the bash sources directly)
bash tests/run_sh_tests.sh

# Cross-platform equivalence (Python + bash compared head-to-head)
python tests/run_equivalence_test.py

Four suites: Python guard/CLI tests, bash-subprocess equivalence, a bash-native harness, and a head-to-head equivalence runner. All must be green before a release is tagged; run them rather than trusting a number printed here. CI runs the full suite on Linux, macOS, and Windows (Python 3.11–3.14); platforms outside that matrix (e.g. BSD) aren't verified.

Quality process. agent-shield is AI-assisted by design — built with AI agents under human architectural direction — and engineered accordingly: changes are developed test-first (red → green) and must pass all four suites before a release is tagged. Releases additionally go through an independent adversarial review pass, with reviewers tasked to break the changes rather than approve them — for v0.2.0 that pass uncovered issues in early hardening attempts, which were corrected and re-verified before shipping. Platform note: the bash-subprocess pytest cases shell out to bash. On Windows the harness resolves Git-Bash/Cygwin explicitly and never the WSL bash.exe shim — Python's subprocess otherwise mis-resolves a bare bash to that shim (Win32 searches System32 before PATH), and it hangs when driven from native-Windows Python. So the parity cases now run on Windows too; if no usable bash is found they skip with a clear reason instead of hanging. Set AGENT_SHIELD_TEST_BASH to a specific bash.exe to override resolution.

---

Troubleshooting

• ModuleNotFoundError: No module named 'agent_shield' — the hook/CLI is running under a different Python than the one agent-shield is installed in. Activate the right venv, or point the hook at that interpreter (e.g. /path/to/venv/bin/python -m agent_shield.bash_guard).
• ModuleNotFoundError: No module named 'agent_shield' after uninstalling — pip uninstall removed the package, but the two PreToolUse hook entries are still in your settings.json, so every tool call now fails. If the agent-shield-plugin command is still available (Claude Code only), run it first: agent-shield-plugin disable (or agent-shield-plugin --project <dir> disable) to remove them safely. If the CLI is gone because the package was already uninstalled, manually edit the file and remove the two PreToolUse entries for Bash and Write|Edit|MultiEdit that call agent_shield.bash_guard / agent_shield.write_guard. Then restart. Full steps: INSTALL_AGENT.md Step 6. If you wired during an earlier alpha, your settings may contain a non-functional legacy shape (hookEventName/toolNamePattern) — migrate via docs/MIGRATION.md.
• A safe command produces no output — that's correct: allow is a silent pass (empty stdout, exit 0). Test with a known-bad command (echo '{"tool_input":{"command":"rm -rf /"}}' | python -m agent_shield.bash_guard) to see a deny.
• Hooks don't take effect in Claude Code — restart the session after editing settings.json; hooks are read at startup.
• Windows: the bash-parity tests are slow or skip — the suite resolves Git-Bash/Cygwin explicitly (never the WSL bash.exe shim) and skips cleanly if no POSIX bash is found; set AGENT_SHIELD_TEST_BASH to a specific bash.exe to override. The runtime guards are pure Python and need no bash.

---

Security model

agent-shield's threat model:

• Adversary: the AI agent itself, when misled by an adversarial prompt, by an injected payload in retrieved content, or by a confused-deputy attack via the harness.
• Trust boundary: the host. The shield runs outside the agent (as a PreToolUse hook) and gets a chance to veto before commands run.
• What it protects: the user's machine, credentials, and host filesystem.
• What it does NOT protect: the agent itself. An agent that decides to refuse work, leak information in its responses, or take subtler malicious actions the shield doesn't see — those failure modes remain.

The shield is defense-in-depth, not defense-as-perimeter. Even with all 8 layers shipping, you should still:

• Run the agent with least-privilege OS permissions
• Keep secrets in a vault, not in environment variables the agent can read
• Treat the audit log as auditable evidence, not as the protection layer

---

Bypasses and limitations

Layer 4 is a best-effort regex layer, not a sandbox. It performs deterministic pattern-matching within its known pattern set; it raises the cost of accidental and naive-adversarial damage, but it does not make damage impossible. Be honest with yourself about what that buys you:

Known evadable forms (deliberately out of scope for pattern matching):

• Variable indirection — assembling the command string at runtime from pieces, so no static pattern ever sees it whole.
• Interpreter hop — wrapping the destructive call inside another language's inline-eval flag (python -c, perl -e, node -e), where the regex never sees it.
• Fetch-then-execute in separate steps — download to a file in one command, then execute it in the next. Each step looks individually mundane.
• Aliases and functions — defining an alias or shell function for a destructive verb, then invoking the harmless-looking name.
• Novel encodings — we catch base64 -d | sh and openssl enc -d | sh; arbitrarily creative encode/decode chains are unbounded.
• Separator/word-split spellings and capability-equivalent verbs — substituting the literal whitespace the patterns key on (shell field-separator / word-splitting tricks), or reaching the same destructive effect through utilities outside the matched verb set. These need OS-level sandboxing, not regex.

Behavioral limitations (by design, documented so you can plan around them):

• Unparseable hook input is allowed. If stdin isn't valid JSON, there is no command to evaluate; the guard exits 0 silently rather than blocking everything. (BOM and UTF-16 input — the common Windows pipeline encodings — are decoded and evaluated, not dropped.)
• No-Python bash fallback degrades asymmetrically. The bash sources' sed-based extraction can truncate complex quoting; in that mode RED checks also scan the raw hook JSON (fail-closed for the dangerous tier), while YELLOW checks run only on what was extracted.
• Bash-port parameter expansion (no-Python / bash-source path). The bash sources inspect a command via echo "$CMD", so a literal $VAR in the inspected command undergoes the shell's own parameter expansion before matching — a value set in the guard's own environment could be substituted. The Python port (the default path) has no such behavior; the common attack patterns are still caught.
• Substring conservatism. Destructive-verb patterns are anchored to command position to avoid flagging grep mkfs log, but a guard built from regexes will always have residual false-positive/false-negative trade-offs at the margins.

If your threat model requires guarantees against a determined adversarial agent, you need OS-level sandboxing (containers, VMs, seccomp, restricted users) underneath this layer. agent-shield is the seatbelt, not the cage.

---

Known gaps

agent-shield is honest about scope. Deterministic pattern-matching within its known pattern set; it is not a sandbox and does not prevent semantic prompt injection.

---

Why you can trust this

agent-shield is a security tool from an independent author — so it's built to be verified, not taken on faith:

• Auditable by design. Zero runtime dependencies — Python ≥3.11 standard library only. The guards are a handful of readable modules, with an inline threat-model comment on every pattern; you can read the whole enforcement surface before you trust it.
• It never phones home. The shipped package makes no outbound network calls — its only socket use is a local gethostname() for the audit record. The optional audit anchor writes to a local path only; to send anything off-box you supply your own shipper (your code, your policy). The no-egress property is checked by a best-effort AST lint in the test suite.
• Test-first, then adversarially reviewed. Changes are developed red→green; every release additionally goes through an independent review pass tasked with breaking the change rather than approving it. The full test suite — guard logic, CLI/hook contract, security regressions, doc-claims, and Python↔bash decision-equivalence — must be green before any release is tagged, and the pattern counts quoted in this README are themselves test-asserted, so code and docs can't drift.
• It protects itself. Writes to its own guard modules and to your hook / permission config are blocked, so an agent under the shield can't quietly disable it.
• The audit trail is tamper-evident. The forensic log is hash-chained — any edit, insertion, deletion, or reorder is detectable (and tamper-resistant when anchored to an independent target).
• Honest about scope. Apache-2.0, alpha-labeled, and explicit about what it does not do — see Bypasses and limitations, the Security model, and SECURITY.md.
• Provenance. Built by esoteric1entity — a PDuk Brainworks project — AI-assisted under human architectural direction. Vulnerability disclosure policy: SECURITY.md.

---

Project status

v0.2.0 alpha — Layers 1, 2, 3, 4, 6, and 7 ship. Layers 0 and 5 are in active development. Python 3.11+ (LTS); tomllib stdlib → zero external deps. Layer 4 runs on a harness-agnostic architecture: one neutral decision core with two functional adapters today — Claude Code (CI-verified) and OpenClaw (live; enforcement requires a recent gateway — see docs/adapter_status.md). Additional harnesses are roadmap.

Sibling package: ultimate-memory-stack — persistent memory for AI agents. The two are independent — each ships and runs on its own.

---

Authors

• esoteric1entity — architect + design lead. A PDuk Brainworks project.
• See AUTHORS.md for the full contributor list.

---

License

Apache License 2.0. Copyright © 2026 esoteric1entity.

---

Citing this work

Everything here is free under Apache-2.0 — no strings attached. If agent-shield helps keep your agent safe, the one thing we ask — entirely optional, always appreciated — is a citation or mention of esoteric1entity / PDuk Brainworks: a link back to this repo, a line in your credits, or GitHub's "Cite this repository" button (powered by CITATION.cff).

---

Documentation

• examples/ — Working integration examples (Claude Code settings, library use, CLI pipe, before/after scenarios)
• CONTRIBUTING.md — How to contribute
• SECURITY.md — Vulnerability reporting policy + threat model
• CHANGELOG.md — Version history
• CLA.md — Contributor License Agreement
• NOTICE — Copyright + acknowledgements
• INSPIRATIONS.md — Projects we built on