Snyk

Evo by Snyk is a security and governance platform for AI-native applications, agentic development, and AI systems. It provides continuous visibility, governance, testing, and real-time control across the AI software lifecycle. Its Agentic Development Security capability discovers models, agents, MCP servers, datasets, plugins, tools, and services used by agents across repositories; generates an AI bill of materials; applies policy and guardrails to agent actions; and validates AI-generated code before it reaches repositories, pipelines, or production. Its Continuous Offensive Security capability autonomously stress-tests applications and AI systems like an adversary, with agents coordinating reconnaissance, vulnerability hunting, exploit validation, remediation, and reporting, and an independent validation model intended to confirm exploitability before reporting findings. Testing is intended to run on code changes and pull requests.

Cynative is an open-source framework and CLI for building security agents with live, read-only access to code, cloud, and runtime infrastructure. It reasons across GitHub, GitLab, AWS, GCP, Azure, and Kubernetes as one system, using frontier models to investigate questions such as exposed resources, privilege escalation paths, infrastructure drift, and leaked credentials. For each investigation, Cynative generates and runs code in an ephemeral sandbox, queries APIs in parallel, cross-checks findings against live evidence, and traces findings to their origins. Its action gate resolves each call to the required IAM actions and applies a read-only policy before attaching credentials; the video also describes an auditable JSONL log of tool calls. Agents are defined as Markdown files containing a description and prompt, and can run interactively, non-interactively, or with piped findings. The project is distributed as a single open-source binary and supports configuring the model provider and model through environment variables.

Sonar is a software code-quality and code-security company whose products provide automated code review and verification from the IDE through CI/CD pipelines. Its platform uses a Guide–Verify–Solve model: teams set coding context and constraints, review code for reliability, security, quality, and compliance through algorithmic and agentic verification, and generate fixes for detected issues and technical debt. It also provides agentic CI verification and background code-maintenance loops intended to validate AI-generated code and improve the operating practices, tooling, and roles used by engineering teams adopting AI agents.

SkillSpector is an open-source security scanner for AI agent skills, developed by NVIDIA. It scans Git repositories, URLs, ZIP archives, directories, and individual skill files before installation, looking for prompt injection, data exfiltration, privilege escalation, supply-chain issues, dangerous code, MCP permission problems, tool poisoning, and other malicious patterns. Its two-stage pipeline combines static analysis—including regex, Python AST, taint tracking, YARA signatures, and OSV.dev dependency lookups—with optional LLM-based semantic evaluation. It produces terminal, JSON, Markdown, or SARIF reports containing findings, severity labels, a 0–100 risk score, and installation recommendations. Baselines can suppress accepted findings, and the CLI can be used in CI or install gates through exit codes and machine-readable output. SkillSpector can also run as an MCP server callable by compatible agents. It does not execute scanned skills or sandbox them; optional LLM analysis sends eligible file contents to the configured provider, while dependency checks query OSV.dev. The project is distributed under the Apache License 2.0 and is part of NVIDIA's Verified Skills pipeline.

YAGNI is a coding-agent platform for engineering teams, offered as a CLI and desktop client. Its agents are grounded in a company’s data and use a shared decision ledger, while per-developer budget controls govern usage. The platform routes requests across vetted, US-hosted open-weight models and supports developer skills, plugins, and custom components; it advertises lower per-call inference costs than frontier APIs.