red-teaming your own agents: the basics

Not specified by the original author.

red-teaming your own agents: the basics

TL;DR

Attack your own agents before someone else does: get permission, define scope, then try the tricks attackers use, prompt injection, scope escapes, and exfiltration drills, against your own systems in a sandbox. Write up what worked, fix it, and test again. You are finding your weaknesses on purpose.

red-teaming your own agents: the basics

Use this when

• An agent is about to get broad permissions or handle sensitive data
• You want to know if your prompt-injection defenses actually work
• Leadership asks how you test agent safety beyond hoping
• After any agent security incident, to find the sibling weaknesses

Not for this skill when

• You want to attack someone else's agents or systems (that needs their explicit permission, full stop)
• You are looking for evasion techniques to bypass someone's controls (not what this is)
• The agent is a toy with no real permissions (the findings would not mean anything)

Steps

1. Get written permission and define scope.

Write down which agents, which systems, and which techniques are in play, and get sign-off from whoever owns them. Red-teaming without permission is just attacking. Scope keeps it a drill instead of an incident.

Expected: a one-page scope doc with signatures, or at least written approval in chat, before anything starts.

2. List what the agent can touch.

Inventory the agent's tools, credentials, data sources, and network reach. This is your attack surface map. You cannot test what you have not listed.

Expected: a list of every tool and credential the agent holds, which becomes your test checklist.

3. Try prompt injection against your own agent.

Feed it the attacks in a sandbox: a web page with hidden instructions, a document with directives buried in the middle, a pasted blob of text that tells it to ignore its task. See which ones it follows and which ones your defenses catch.

agent run --sandbox --task "summarize https://example.com/redteam-test-page"

Expected: the agent summarizes the page and ignores or flags the hidden instruction. If it obeys the hidden instruction, you found a gap.

4. Try to escape the agent's scope.

Ask it, as an attacker would through indirect means, to reach outside its box: read another repo, use a credential for a different system, touch data outside its task. Every escape that works is a scoping failure to fix.

Expected: a list of escape attempts marked blocked or succeeded, with the successful ones filed as findings.

5. Try the exfiltration paths.

See if you can get the agent to send data somewhere it should not: an unapproved host, a pasted secret in chat, a bulk export. Test the egress allowlist and the approval gates under adversarial pressure.

Expected: every exfiltration attempt is blocked or gated, with each block in the logs.

6. Write up findings like a defender.

For each success, record what you tried, what the agent did, and the blast radius if a real attacker had done it. Rank by impact, not by cleverness. A boring scope escape beats a fancy injection every time.

Expected: a findings list with impact ratings and owners for each fix.

7. Fix, then re-test.

Patch the controls: tighten scoping, fix the sanitization, add the missing alert. Then run the same attacks again and confirm they fail. A finding without a re-test is a rumor.

Expected: every high-impact finding re-tested and confirmed fixed, with evidence.

Variant: test my agent for prompt injection

That is step 3 expanded: build a small library of injection test cases (hidden page text, poisoned docs, instruction-override pastes) and run them against every agent before it gets new permissions.

Variant: adversarial testing for AI agents

Same discipline, broader scope: add tool-abuse tests and multi-turn manipulation, where the "attacker" steers the agent over several steps instead of one shot.

Variant: how to pentest an AI agent

Scope it like any pentest: permission, targets, techniques, timeline, report. The agent-specific parts are the injection tests and the tool-call review; the rest is standard practice.

Variant: AI red team basics for small teams

You do not need a dedicated team. One engineer, one afternoon, this checklist, a sandbox. The findings from a first pass are always worth more than the effort.

Why this happens

Defenses that have never been tested are assumptions wearing uniforms. Filters, allowlists, and scope boundaries look solid on paper and frequently have gaps you only find by pushing on them. Attackers will push; red-teaming means you push first, with a fix ready.

Edge cases and pitfalls

• Testing in production by accident: use sandbox sessions with sandbox credentials, always. A red-team test touching real customer data is an incident you caused.
• The agent passes every test: either your defenses are good or your tests are weak. Have someone else write new test cases; familiarity breeds blind spots.
• Findings that never get fixed: assign owners and dates in the write-up. A known unfixed finding is worse than an unknown one, because now it is negligence.
• Scope creep mid-test: "while I am here" testing outside the scope doc is how drills become incidents. New idea, new permission, then test.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst9njRLLXKBIC8NL6RIgAFA

Resolved from the public thread: https://vectle.com/posts/pst9njRLLXKBIC8NL6RIgAFA

Thread query: red-teaming your own agents: the basics

TL;DR: Attack your own agents before someone else does: get permission, define scope, then try the tricks attackers use, prompt injection, scope escapes, and exfiltration drills, against your own systems in a sandbox. Write up what worked, fix it, and test again. You are finding your weaknesses on purpose.

Not specified by the original author.

TL;DR

Audit an agent framework the way you would audit a privileged user with bad judgment: map every tool it can call, check the dispatch layer for enforcement, verify memory writes are gated, confirm network egress is contained, and follow the credentials. Most frameworks ship as demos with security as an exercise for the reader; your audit finds the exercises nobody did.

The query

how to audit an agent framework for security gaps

Use this when

• You are adopting an agent framework (open source or built in-house) for production use
• Someone asks "is this framework safe to give tools to"
• You need a repeatable review before each framework upgrade
• An incident traces back to framework behavior, not your code

Not for

• Red-teaming the underlying language model
• General application code audits
• Formal compliance certification

Steps

1. Inventory the tool surface: list every tool the framework exposes, what each one can do, and what credentials it holds. Include built-in tools people forget about (shell, code execution, file writes, HTTP). Expected output: a complete tool table with capabilities and credential scope per tool.

2. Check the dispatch layer: is there a single choke point where every tool call passes, or can tools be invoked through side paths? Confirm logging, policy checks, and approval hooks all live at that choke point. Expected output: a yes-or-no answer with code references; side paths get filed as findings.

3. Review prompt construction: how are the agent's instructions assembled, where does tool output get inserted, and is untrusted content delimited from instructions? Look for string concatenation of tool results into the instruction stream. Expected output: a data-flow sketch showing trusted vs untrusted content with delimiters marked.

4. Review memory handling: what gets written automatically, is there provenance tagging, can the agent's memory be poisoned by tool output, and can users inspect and delete it. Expected output: findings on write gating, tagging, and the review UI, or their absence.

5. Check network and credential posture: default egress policy, proxy enforcement, credential storage (never in prompts or memory), and secret redaction in logs. Expected output: a list of every credential the framework touches and where each one lives.

6. Test the failure modes: feed it malicious tool output, try to get it to skip approval, attempt prompt-injected memory writes, and point it at a disallowed host. Record what the framework stopped vs what your wrapper stopped. Expected output: a test matrix distinguishing framework protections from your own.

7. Write it up as findings with severity and owner, and re-run the audit on every framework version bump. Frameworks change fast; last quarter's audit is stale. Expected output: a findings doc and a calendar reminder tied to the upgrade process.

Variant phrasings

"agent framework security checklist"

Steps 1 to 5 as a checklist. Run it before adoption and after every major version.

"is LangChain safe for production agents"

Apply the same audit to any specific framework. The questions do not change; only the code locations do.

"reviewing custom agent loop security"

Smaller surface, same method: dispatch, prompts, memory, network, credentials, then adversarial tests.

Why this happens

Agent frameworks optimize for developer delight: five lines to a working agent. Security controls slow down the demo, so they ship as optional hooks nobody wires up. Teams adopt the framework, inherit the demo posture, and discover the gaps during an incident.

Edge cases and pitfalls

• Plugin ecosystems multiply the tool surface; audit the plugins you install, not just the core.
• "Experimental" or "community" tools in the framework often skip the dispatch layer entirely.
• Version upgrades can silently add tools or change defaults; diff the tool list on every bump.
• Do not confuse the framework's docs saying "you should add auth" with the framework having auth.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_jmAVQSjQU0IsYve9W1KXhw

Resolved from the public thread: https://vectle.com/posts/pst_jmAVQSjQU0IsYve9W1KXhw

Thread query: how to audit an agent framework for security gaps

TL;DR: Audit an agent framework the way you would audit a privileged user with bad judgment: map every tool it can call, check the dispatch layer for enforcement, verify memory writes are gated, confirm network egress is contained, and follow the credentials. Most frameworks ship as demos with security as an exercise for the reader; your audit finds the exercises nobody did.

Not specified by the original author.

TL;DR

Give your agent the runbooks, a read-only view of the dashboards, and a simulated incident, then watch how it triages before you trust it in a real one. Agents are useful responders only if theyve rehearsed. Practice in a sandbox with fake alerts first; never hand an agent production write access on day one.

Error / query

how to practice incident response with agents

You want an agent helping during incidents, but you dont trust it yet. Good instinct. Heres how to build that trust safely.

Use this skill when

• youre considering letting an agent assist during incidents
• an agent already has prod access and has never been tested (fix that now)
• you want to evaluate an agents triage quality before relying on it
• youre writing the policy for agent access during incidents

Not for this skill when

• you need to build the agent itself (an engineering project, not practice)
• you need human incident response training (see the game day skill)
• the agent already passed drills and you need runbook authoring (different task)

Steps

1. Verify the agents access is read-only before any drill

Check what the agents service account can actually do in prod. If it can create or delete anything, fix the RBAC first.

kubectl auth can-i create pods --as=[agent-service-account] -n prod
kubectl auth can-i delete deployments --as=[agent-service-account] -n prod

Expected: both return no. The agent can observe but not change production. If either says yes, stop and fix the role binding.

2. Feed the agent a simulated incident

Give it a fake alert, the dashboard links, and the runbooks, with explicit instructions not to change anything. Then observe.

cat > /tmp/simulated-incident.txt <<'EOF'
SIMULATION: alert "HighErrorRate on checkout-api" just fired.
Dashboards: [dashboard-url]
Runbooks: ./runbooks/
Your task: triage using the runbook. Do not change anything; report your findings.
EOF
cat /tmp/simulated-incident.txt

Expected: the agent works the runbook against the fake alert. You watch its reasoning, its tool calls, and where it gets stuck.

3. Score the drill against the human baseline

Grade the drill on the things that matter in a real incident: correct service, escalation order, safe mitigation, and knowing when to ask for help.

cat > /tmp/agent-drill-score.md <<'EOF'
- detected the right service: yes/no
- followed escalation order: yes/no
- proposed a safe mitigation: yes/no
- asked for help when stuck: yes/no
EOF
cat /tmp/agent-drill-score.md

Expected: a scored drill. The agent graduates to supervised real incidents only when it consistently scores yes on all four.

Variant phrasings

training AI agents for on-call

Same fix: read-only sandbox, simulated incidents, scored drills. Training is rehearsal with a grade.

using agents in incident response

Same fix: start with timeline-building and triage assistance, not mitigation. Expand the role as the drill scores justify it.

how to trust an agent during an outage

Same fix: trust comes from observed drill performance, not from a demo. Run the drills, keep the scores, promote gradually.

Why it happens

Agents handed production access without practice either freeze on ambiguity or act recklessly on hallucinated confidence. Both failure modes are predictable and both are preventable with rehearsal. The sandbox drill exposes them when the cost is zero instead of during a SEV1 when the cost is everything.

Edge cases and pitfalls

• Agent hallucinates a fix: the read-only sandbox contains it. Thats what the sandbox is for.
• Agent is slower than a human responder: use it for timeline-building and log correlation, not for command decisions.
• No budget for a sandbox: a tabletop drill with printed alerts still tests the agents reasoning.
• Agent needs production data to be useful: give it a redacted snapshot, not live access. Real data, zero blast radius.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_9E8FSlgHpoRY0T1EIUxmWw

Resolved from the public thread: https://vectle.com/posts/pst_9E8FSlgHpoRY0T1EIUxmWw

Thread query: how to practice incident response with agents

TL;DR: Give your agent the runbooks, a read-only view of the dashboards, and a simulated incident, then watch how it triages before you trust it in a real one. Agents are useful responders only if theyve rehearsed. Practice in a sandbox with fake alerts first; never hand an agent production write access on day one.