Results for “ai-agent”

Authenticate AI agents with services using ERC-8004 onchain identity via Sign-In With Agent (SIWA).

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Configure Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for caching, rate limiting, content safety, and cost control.

Scans AI agents, MCP servers, and skills for security vulnerabilities from the command line, detecting prompt injections, tool poisoning, toxic flows, malware payloads, and credential handling issues across 15+ risk categories.

Discovers, installs, and manages AI agent skills from agentskill.sh, including searching, installing mid-session, scanning for security issues, and providing feedback.

Apply least-privilege tool allowlisting, identity binding, and human-in-the-loop controls for agent tool calls.

Verify supply chain integrity for AI agent plugins, tools, and dependencies by generating SHA-256 manifests, detecting tampered files, auditing dependency pinning, and enforcing promotion gates.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Safety guardrails for autonomous agents. Intercepts destructive actions and vets skills, requires structured approval before irreversible operations, with CLI-backed cryptographic audit trails and policy enforcement.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Scan and audit AI agent skills for security risks before installation, producing a PASS/WARN/FAIL verdict with findings and remediation guidance.

Scans local repos and AI agent skill folders for security drift, saves trusted baselines, and compares changes after updates to highlight risky new capabilities.

Create and manage Ethereum wallets, including EOAs, smart contract wallets, Safe multisig, EIP-7702, and account abstraction, with AI agent key safety rules and transaction approval thresholds.

Code review is time-consuming and inconsistent. Reviewers miss security issues, overlook edge cases, or focus on style over substance. Meanwhile, AI agents catch these mechanical issues instantly but can't judge business logic correctness, UX decisions, or architectural fit.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

Read-only audit for the non-functional "20%" AI agents systematically skip: timeouts, retries with backoff+jitter, circuit breakers, idempotency keys, rate limiting, graceful. Use when "is this production-ready", "resilience audit", "will this survive real traffic", "audit retries/timeouts/idempotency", "reliability.

Automate multi-turn adversarial conversations against LLM agents using Microsoft PyRIT, including Crescendo and Tree-of-Attacks-with-Pruning (TAP) attack chains with scorer feedback loops.

Scans installed or specified agent skills for advertising, covert commercial intent, task hijacking, suspicious external calls, and sensitive-data access. Reports findings first and quarantines only after explicit user confirmation.

Runs a structured security audit across six phases: attack-surface mapping, STRIDE/PASTA threat modeling, technical checklists, red/blue team exercises, and a final verdict, covering code, infrastructure, APIs, bots, payments, AI agents, and compliance.

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers.