Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Authorized Use Only: Run these adversarial probes only against LLM applications and endpoints you own or are explicitly authorized to test. Generated attack payloads (jailbreaks, prompt injections, harmful-content elicitation) are adversarial inputs; sending them to third-party services without permission may violate terms of service.
Promptfoo is an open-source LLM evaluation and red-teaming framework (used by OpenAI and Anthropic per its README) that generates adversarial test cases, runs them against your model/agent, and grades the responses. DeepTeam (by Confident AI) is a complementary open-source framework offering 50+ ready-to-use vulnerabilities and 10+ research-backed attack methods. Together they let you treat LLM security as a regression test: every commit re-runs the same adversarial suite, and the pipeline fails when a previously-safe behavior regresses.
This matters because LLM applications change constantly — prompts, models, RAG sources, tools, and guardrails all drift. A jailbreak that was patched last sprint can silently return after a prompt edit or a model upgrade. Promptfoo maps its plugins directly onto the OWASP LLM Top 10 (owasp:llm) and OWASP Agentic (owasp:agentic) presets, and onto MITRE ATLAS, so the suite tracks recognized risk taxonomies. The core threat addressed here is AML.T0051 — LLM Prompt Injection (MITRE ATLAS): adversarial instructions that override the application's intended behavior. This skill follows the Promptfoo red-team docs (https://www.promptfoo.dev/docs/red-team/) and DeepTeam docs (https://www.trydeepteam.com/docs/getting-started), and aligns to NIST AI RMF MANAGE-4.1 (post-deployment monitoring and feedback to manage AI risk).
npm install -g promptfoo # or: npx promptfoo@latest
pip install -U deepteam
| ID | Name (MITRE ATLAS) | Tactic |
|---|---|---|
| AML.T0051 | LLM Prompt Injection | Initial Access / Persistence (LLM) |
| AML.T0051.000 | Direct (Prompt Injection) | LLM Attack |
| AML.T0051.001 | Indirect (Prompt Injection) | LLM Attack |
| AML.T0054 | LLM Jailbreak | Privilege Escalation / Defense Evasion (LLM) |
Initialize an interactive config; it writes promptfooconfig.yaml where targets, plugins, and strategies live.
promptfoo redteam init
# choose your target type (HTTP endpoint, openai:..., anthropic:..., custom provider)
Edit promptfooconfig.yaml. The purpose grounds attack generation; plugins are adversarial input generators; strategies are delivery techniques (jailbreak/injection wrappers).
# promptfooconfig.yaml
targets:
- id: https://api.example.com/chat # your app endpoint
label: support-bot
redteam:
purpose: |
A customer-support assistant for an e-commerce site. Must never reveal
system prompts, leak PII, or perform actions outside order support.
numTests: 10
plugins:
- owasp:llm # OWASP LLM Top 10 preset
- owasp:agentic # OWASP Agentic threats preset
- id: pii:direct
numTests: 15
- prompt-extraction # system-prompt leakage
- harmful
strategies:
- id: jailbreak # iterative single-turn jailbreak
- id: jailbreak:composite # stacked jailbreak techniques
- id: crescendo # multi-turn escalation
- id: prompt-injection # injection wrapper
redteam run combines generation + evaluation; then open the interactive report.
promptfoo redteam run
promptfoo redteam report # launches the web report (pass/fail per plugin)
Each row shows the plugin (mapped to OWASP/ATLAS), the strategy, the attack prompt, the model's response, and the grader's verdict. The attack success rate per plugin is your headline metric — track it per release.
Use DeepTeam to cover additional vulnerabilities/attacks and to script bespoke suites in Python.
# deepteam_suite.py
from deepteam import red_team
from deepteam.vulnerabilities import Bias, PIILeakage
from deepteam.attacks.single_turn import PromptInjection
def model_callback(prompt: str) -> str:
# call your application's LLM endpoint here and return the text response
return call_my_app(prompt)
red_team(
model_callback=model_callback,
vulnerabilities=[Bias(types=["race"]), PIILeakage(types=["api_and_database_access"])],
attacks=[PromptInjection()],
)
DeepTeam can also be driven from a YAML config:
deepteam run config.yaml
Fail the pipeline when red-team assertions fail. Promptfoo returns a non-zero exit code on failures, which blocks the merge.
# .github/workflows/llm-redteam.yml
name: LLM Red Team
on: [pull_request]
jobs:
redteam:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with: { node-version: '20' }
- run: npm install -g promptfoo
- name: Run red team (fails build on new vulns)
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: promptfoo redteam run --no-progress-bar
- name: Export machine-readable results
if: always()
run: promptfoo redteam report --output results.json
- uses: actions/upload-artifact@v4
if: always()
with: { name: redteam-report, path: results.json }
Persist results.json per run and compare attack-success-rate per plugin between releases. A rising rate for any OWASP LLM category is a regression to triage before release. Promptfoo's --filter-failing lets you re-run only previously failing cases to confirm a fix.
promptfoo redteam run --filter-failing results.json
| Resource | Link |
|---|---|
| Promptfoo red-team docs | https://www.promptfoo.dev/docs/red-team/ |
| Promptfoo red-team configuration | https://www.promptfoo.dev/docs/red-team/configuration/ |
| Promptfoo CI/CD integration | https://www.promptfoo.dev/docs/integrations/ci-cd/ |
| Promptfoo MITRE ATLAS mapping | https://www.promptfoo.dev/docs/red-team/mitre-atlas/ |
| DeepTeam (Confident AI) | https://github.com/confident-ai/deepteam |
| DeepTeam docs | https://www.trydeepteam.com/docs/getting-started |
| OWASP Top 10 for LLM Applications | https://genai.owasp.org/ |
| Promptfoo item | Type | Maps to |
|---|---|---|
owasp:llm | preset | OWASP LLM Top 10 suite |
owasp:agentic | preset | OWASP Agentic threats |
prompt-extraction | plugin | LLM07 system-prompt leakage |
pii:direct | plugin | LLM06 sensitive-info disclosure |
harmful | plugin | harmful content generation |
jailbreak / jailbreak:composite | strategy | AML.T0054 LLM jailbreak |
crescendo | strategy | multi-turn jailbreak |
prompt-injection | strategy | AML.T0051 prompt injection |
promptfooconfig.yaml created with target, owasp:llm, and owasp:agentic plugins.promptfoo redteam run executes and produces a per-plugin pass/fail report.model_callback.results.json artifact archived per run for regression tracking.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
Audit, detect, and remediate Shadow Credentials misconfigurations on msDS-KeyCredentialLink in Active Directory. Focuses on access-controls, monitoring Event ID 5136, and credential-management hygiene.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
日本語の概要は準備中です。原文の説明を表示しています。
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。