「prompt-injection」の検索結果

Prompt injection detection and prevention for secure LLM applications

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。

Authors Vally conformance tests for prompts, instructions, agents, and skills, and refuses jailbreak, prompt-injection, harmful-elicitation, TOS, CoC, and PII-extraction stimuli

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。

Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries.

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。

This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".

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。

LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments

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 applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when the user mentions 'prompt injection,' 'LLM security,' 'AI security,' 'jailbreak,' 'indirect prompt injection,' 'prompt leaking,' 'AI red team,' 'LLM vulnerabilities,' 'AI input validation,' 'system prompt extraction,' 'agent security,' 'MCP security,' 'AI permissions,' 'AI privilege escalation,' or needs to secure any application with AI features, AI agents, or LLM integrations.

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。

Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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。

Wiki-first "what do I use" lookup - pick the automated TOOL (wiki/tools/), then the PAYLOAD/technique (wiki/payloads/ + wiki/cheatsheets/), for a surface/service/vuln-class BEFORE hand-rolling or working from memory. Use for "tool for <service>", "automated tools for web/<service>", "what should I run on <surface>", "which tool for <X>", "payloads for <X>", "payload arsenal", "cheatsheet for <X>", "how do I exploit <tech/class>", "exploit/attack chain for <X>", "arsenal", any SSRF/XSS/SQLi/SSTI/LFI/JWT/XXE/IDOR/NoSQL/deserialization/CSRF/CORS/CRLF/GraphQL/smuggling/web-cache/OAuth/SAML/MFA/crypto/LDAP/XPath/WebAuthn/file-upload/IMDS/prompt-injection/Modbus ask, plus "privesc arsenal", "CVE arsenal", "default creds", "nuclei templates", "sqlmap/hydra/nmap/bloodhound", "password attacks".

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。

Assessing privacy risks in large language model outputs including training data memorisation, PII leakage in generated text, prompt injection leading to data extraction, and hallucinated personal data. Covers output filtering, guardrails, and monitoring. Keywords: LLM privacy, output risk, memorisation, PII leakage, prompt injection, hallucinated PII.

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 agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk. Use when evaluating a third-party skill before installing, enabling, updating, publishing, or distributing it; reviewing an untrusted SKILL.md, agent configuration, MCP integration, archive, or repository; comparing a package with a known-good version; or investigating unexpected tool, network, credential, or filesystem behavior.

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。

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-testing.

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。

Implement comprehensive safety guardrails for LLM applications including content moderation (OpenAI Moderation API), jailbreak prevention, prompt injection defense, PII detection, topic guardrails, and output validation. Essential for production AI applications handling user-generated content. Use when ", guardrails, content-moderation, prompt-injection, jailbreak-prevention, pii-detection, nemo-guardrails, openai-moderation, llama-guard, safety" mentioned.

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。

Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.

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。

Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).

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。

Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.

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。

Automated security scanner for external skill/agent content fetched from GitHub or web sources. Runs a 7-step PASS/FAIL security gate against fetched markdown/text content.

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。

Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.

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。

LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass techniques.

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。

Exploit AI assistants equipped with web-browsing capabilities or internal API plugins to perform Server-Side Request Forgery (SSRF). This skill details injecting prompts that force the LLM to request sensitive internal endpoints, such as underlying cloud metadata services or internal networks.

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。

Bypass AI safety filters by encoding malicious prompts using ciphers and obfuscation techniques (e.g., Base64, ROT13, Leetspeak, Morse code). This skill exploits the gap where the LLM can decode the request, but intermediate keyword-based safety classifiers cannot.

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。

AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, token budgets, retry/timeout handling, streaming, and key/PII exposure.

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。

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).

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。