「prompt injection」の検索結果

Benchmark external agent frameworks, auto-detect source type, scan for prompt injection, and convert findings into a concrete TDD upgrade backlog for agent-studio evolution.

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Run AI agent tasks on a Volcengine (火山引擎) cloud phone via the Mobile Use Agent OpenAPI. Use when the user asks to drive, automate, operate, or test an Android app on a cloud phone for enterprise use cases (e.g. reply to WeCom customer messages, add leads in a CRM, clock in on DingTalk, check invoicing stats, query inventory in a supply-chain app, ship e-commerce orders, approve OA requests) or personal ones (open 小红书 and search, place an order, check nearby places on a map), or to check status, fetch result, cancel, or list a Mobile Use run. Requires one-time setup to store Volcengine AK/SK locally; ProductId and PodId can be saved as a default device during setup, the user prompt is provided per run. Supports optional GPS injection (GpsInfo) with user consent (auto-asked only for location-related tasks).

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White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.

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AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models. Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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Guideline for designing, implementing, and verifying secure TypeScript and JavaScript applications following OWASP Top 10 best practices. Use when the user wants to: (1) review TypeScript or JavaScript code for security vulnerabilities, (2) design a secure Node.js, Deno, or browser application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit npm/yarn/pnpm dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing TypeScript or JavaScript code, (7) set up security testing and static analysis (ESLint security plugins, Semgrep, Snyk), or (8) handle any TypeScript/JavaScript security concern including injection prevention, prototype pollution, XSS protection, SSRF prevention, secrets management, and secure deployment.

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Guideline for designing, implementing, and verifying secure Python applications following OWASP Top 10 best practices. Use when the user wants to: (1) review Python code for security vulnerabilities, (2) design a secure Python application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit Python dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing Python code, (7) set up security testing and static analysis (bandit, safety, semgrep), or (8) handle any Python security concern including injection prevention, secure deserialization, SSRF protection, secrets management, and secure deployment.

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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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Prompt-injection and data-exfiltration screening for untrusted text. Use before summarizing web/email/social content, before replying, and especially before writing anything to memory. Provides a safe memory append workflow (scan → lint → accept or quarantine).

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Secure skill template with prompt injection defenses. Copy this template when creating new skills.

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Black-box evaluation of a shipped LLM feature — adversarial probes for hallucination, prompt-injection, and cost-runaway vs stated expectations. Not RAG/embedding. Triggers 'review my chatbot'.

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Operate or troubleshoot Interlateral CLI mesh transport: tmux socket discovery, direct-send helpers, comms.md ledger, identity stamping, idle checks, ACK proof, and safe prompt injection. This is comms mechanics only, not a collaboration workflow.

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Phased risk-centric security assessment framework. Trigger when: - Auditing code for vulnerabilities, checking SQL injection, access control, or buffer overflows. - Prompt contains: /security-audit, security audit, taint analysis, threat model, vulnerability.

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Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

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Designs a pre-launch evaluation plan for a declarative agent from its instructions text, skills, capabilities, knowledge sources and sample requests: a behaviour contract of testable statements quoted from the text, test prompts by scenario family and tier with setup, expected behaviour, pass and fail criteria and red flags, a red-flag catalogue, a blank scoring sheet, a coverage table and a go or no-go checklist the owner completes and decides. Use when the user asks to "write test cases for my agent", "how do I evaluate this agent before launch", "build a test plan for the agent", "what should I check before publishing this agent" or "give me a go no-go checklist". Do not use for finding injection or leakage exposures in the instructions, use agent-instructions-red-team instead; do not use for reviewing one SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks.

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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

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LLM Prompt injection Share Executive Summary Palo Alto Networks has released Securing GenAI: A Com

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LLM) can be manipulated through prompt injection, whether a retrieval pipeline leaks confidential da

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Prompt Injection Challenge: LLMail-Inject The competition features 40 unique levels, each combining

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Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.

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Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapt

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This skill should be used when conducting a scored agent-native architecture review. It launches 8 parallel sub-agents to audit action parity, context injection, CRUD completeness, capability discovery, and prompt-native features.

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。