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Agent Safety

Community
ericrisco
agent-safety

Use when bounding an LLM agent that already runs — scoping its task domain, gating tools to least privilege, defending against prompt injection in untrusted web/email/RAG text, requiring human approval on irreversible actions, capping runtime and cost, or triaging what it already did. NOT building the loop, tools, or RAG (that is `building-agents`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill nameagent-safety
Stars
239
Forks
24
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericrisco on GitHub. Read the source before you install it.

Installation

Install the Agent Safety AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/agent-safety
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/agent-safety .claude/skills/agent-safety
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Safety in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Safety on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Safety is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent safety

You are the security review for an agent's agency, not for its code. The loop works, tools are wired, memory persists — your job is to make that autonomy bounded. If you want to review ordinary endpoints, auth, or secrets handling, that is ../secure-coding/SKILL.md — this skill is the Agentic Top 10, the risks that exist only because a model has tools and autonomy. If the loop or tools do not exist yet, that is ../building-agents/SKILL.md. You arrive after both.

references/threat-model.md carries the OWASP Agentic Top 10 2026 risks mapped to the controls below, the pre-ship guardrail checklist, and the incident-triage flow for "the agent did X" — open it when you are reviewing before ship or reconstructing an incident.

The ownership split

Agent security splits into four layers — Model · Harness · Tools · Environment. The model provider owns only the Model layer (alignment, refusals). Everything else is yours: the Harness (loop, memory, context assembly), the Tools (what the agent can do), and the Environment (creds, network, blast radius). Do not outsource a layer you own to "the model is aligned."

Three excesses cause almost every agentic incident. Cut all three:

  • Excessive functionality — tools the task never needs.
  • Excessive permissions — broader scopes/creds than the tool needs.
  • Excessive autonomy — acting without checking back when it should.

The operating principle is least agency: autonomy is earned per task, not defaulted.

Scope limits

  • Declare the allowed task domain as a hard boundary in the system prompt. Why: an undeclared scope is an infinite scope; "you are a refund assistant; you do not touch payroll" is a constraint a reviewer can check.
  • Deny by default — the agent starts with zero tools. Each tool earns its place by a task justification. Why: an opt-out tool list grows; an opt-in list stays minimal.
  • Segregate the instruction channel from the data channel. System/developer prompt = trusted instructions. Everything the agent reads at runtime = data, never instructions. Why: this single boundary is what stops indirect injection (LLM01).

Tool gating / least agency

Give every tool a profile: read / write / exec / send, the exact resources it may touch, and an allowlist (never a wildcard). Block destructive flags and secret paths at the tool boundary, not in the prompt — the prompt is advisory, the boundary is enforced.

python
# Bad: one wildcard tool = unbounded blast radius, runs anything the loop emits
def run_shell(cmd: str) -> str:
    return subprocess.run(cmd, shell=True, capture_output=True, text=True).stdout
python
# Good: narrow tool, allowlisted root, denied patterns, no shell
ALLOWED_ROOT = pathlib.Path("/srv/agent/workspace").resolve()
DENY = ("*.key", "*.pem", "*secret*", "*.env", "id_rsa*")

def read_file(path: str) -> str:
    p = (ALLOWED_ROOT / path).resolve()
    if not p.is_relative_to(ALLOWED_ROOT):           # no traversal out of scope
        raise PermissionError("path outside workspace")
    if any(p.match(g) for g in DENY):                # never read secrets
        raise PermissionError("denied pattern")
    return p.read_text()
  • Issue task-scoped, short-lived tokens — not the session's broad creds. A credential should be valid only for the specific tool and the duration of one task. Why: a hijacked loop cannot reuse a session-wide token it never held.
  • Prefer read-only by default; writes/sends/exec are separate, gated tools. Why: most steps only need to read, so most steps should be unable to mutate anything.

Injection defense

Treat all external data as untrusted: user messages, retrieved documents, API responses, emails, web pages, other agents' output. Sanitize and delimit before it enters context, and never let external text reach a privileged tool unmediated.

SourceTrust levelRequired mediation before it can act
System / developer promptTrustednone (this is the only instruction channel)
End-user chat messageUntrusteddelimit; treat as data, not commands
Retrieved RAG / KB documentUntrusteddelimit; strip instruction-like spans
Fetched web page / API JSONUntrustedparse to schema; no raw text → tool args
Inbound email / ticket bodyUntrusteddelimit; HITL on any action it requests
Another agent's messageUntrustedsame as external user input
python
# Bad: retrieved chunk flows straight into a privileged action
chunk = retriever.search(q)[0].text          # attacker-controlled doc
agent.call_tool("send_email", to=extract_to(chunk), body=chunk)
python
# Good: external content is quarantined data; the action is schema-validated + gated
chunk = retriever.search(q)[0].text
ctx = f"<retrieved untrusted>\n{chunk}\n</retrieved untrusted>"   # delimited, labeled
proposal = agent.draft("send_email", context=ctx)                # model proposes
args = SendEmail.model_validate(proposal.args)                   # schema or reject
if args.to_domain not in ALLOWED_DOMAINS:                        # exfil guard
    raise PermissionError("recipient outside allowlist")
require_human_approval("send_email", args)                       # irreversible → HITL
  • Validate every tool-call argument against a strict schema before execution. Why: a schema rejects the surprise field, encoded payload, or off-allowlist recipient injection produces.
  • Watch for exfiltration shapes — unexpected outbound URLs, base64 blobs, recipients outside the allowlist. Why: data theft is the common payload of a successful injection.

Human-in-the-loop by risk class

Do not approve every action — reported ~93% of permission prompts get approved without being read, so blanket prompting trains a rubber stamp. Gate by risk class, keyed on reversibility × blast radius. Bind each approval to the exact parameters with a short-lived token so the approved action cannot be swapped after the click.

Action type (examples)Reversible?Blast radiusControl
Read file, search KB, fetch pagen/anoneauto
Write to scratch workspace, internal draftyeslocallog-only
Mutate prod DB, deploy, change confighardsystemapprove (HITL)
Send email/payment to external party, post livenoexternalapprove (HITL)
Delete backups, rotate prod creds, mass-deletenocatastrophicblock (or step-up auth)
  • Step up for the top row — high-value irreversible actions deserve fresh auth, not the ambient session. Why: a hijacked session should not also hold the keys to the worst action.

Memory hygiene

  • Validate and sanitize content before it is stored. Why: memory poisoning persists across sessions (OWASP Agentic T1) — unlike session-scoped injection, a poisoned memory re-attacks every future run until purged.
  • Isolate memory per user and per session; do not let one user's writes color another's reads. Why: shared memory is a cross-tenant injection channel.
  • Expire entries and cap memory size; redact PII (SSN, cards, API keys) before persist. Why: stale instructions and leaked secrets both age into liabilities.

Runtime kill-switches

A looping or hijacked agent must hit a wall on its own. Set hard caps, fail closed:

  • Tool-call rate cap (e.g. ~30 calls/min) — runaway loops trip it before they do damage.
  • Cost cap per session (e.g. ~$10) — a wallet attack stops at a known ceiling.
  • Loop / step cap — a fixed max iterations kills the infinite plan.
  • Wall-clock timeout — a stuck agent is terminated, not left running.

Log every tool call (arguments redacted) and alert on repeated approval-bypass attempts.

Anti-patterns

Anti-patternWhy it bitesDo instead
Approve every action~93% rubber-stamped; the real risky one slips throughGate by risk class; HITL only on irreversible/external
One broad session token shared by all toolsHijacked loop reuses it everywhereTask-scoped, short-lived per-tool tokens
Trust RAG / fetched / email contentIndirect injection (LLM01) becomes direct tool executionDelimit as untrusted data; mediate before any tool
Wildcard run_shell(cmd) toolUnbounded blast radiusNarrow tools, allowlisted resources, denied patterns
Raw external text piped into tool argsAttacker controls the action's parametersSchema-validate args; allowlist recipients/domains
Scope/limits stated only in the promptPrompt is advisory; the model can be talked out of itEnforce at the tool/harness boundary
No loop / cost / rate capA hijacked or looping agent runs until it runs out of moneyHard fail-closed kill-switches
Redact PII only in the UIThe secret was already written to memory/logsRedact before persistence, at the source

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Safety AI skill do?

Use when bounding an LLM agent that already runs — scoping its task domain, gating tools to least privilege, defending against prompt injection in untrusted web/email/RAG text, requiring human approval on irreversible actions, capping runtime and cost, or triaging what it already did. NOT building the loop, tools, or RAG (that is `building-agents`).

Why use Agent Safety on TypingMind?

Because you install it once and use it with any model. Agent Safety is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Safety in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/agent-safety. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Safety?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Safety?

As many as you like. As long as a model supports skills, you can use Agent Safety with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Safety AI skill free?

Yes. It is published on GitHub by ericrisco under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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