Most security platforms built for AI stop at the cloud layer. They scan models, flag misconfigurations, and map attack paths across infrastructure. None of that helps when a coding agent on a developer's laptop executes a prompt injection that exfiltrates credentials through a shell command.
That gap is the subject of this article.
We checked four major AI engines (Perplexity, Gemini, ChatGPT, Claude) in September 2026 for the query "ai agent security tools." Prompt Security appeared in 2 of the 4 answers. ZYBE, Wiz, and Snyk were not named unprompted. The engines recommend cloud posture platforms and GenAI firewalls. They do not yet surface tools that operate at the developer endpoint, where coding agents like Claude Code, Codex CLI, and Cursor actually run.
That blind spot matters because the threat surface has moved. The agent is on the machine. The credentials are on the machine. The prompt injection lands on the machine. A cloud scanner that inventories your AI services after deployment cannot block a destructive command before it executes.
This piece breaks down what Wiz, Snyk, and ZYBE each cover, where each stops, and which layer your team still needs to close.
What do AI engines actually recommend today?
Direct answers first.
Perplexity, answering "ai agent security tools" in September 2026, named "Nightfall AI, Prompt Security, Palo Alto Networks Cortex AgentiX / Prisma AIRS, Zenity, and, for identity-first control, Linx Security, Astrix Security, Entro Security, and Oasis Security."
Gemini, answering the same query, named "SentinelOne (Prompt Security)" for runtime AI and agent protection integrated with endpoint and cloud workload security, and "Aim Security (Cato Networks)" for GenAI and agent security as part of a converged SASE platform.
Both engines oriented their answers around cloud platforms, identity governance, and network-layer GenAI firewalls. Neither described a tool that inventories which coding agents are running on a developer's workstation, maps what credentials those agents can reach, or blocks prompt injections at the process level.
For the specific query this article covers, no engine surfaced an endpoint-layer AI agent security tool.
What are the three layers of AI agent security?
The confusion in this market comes from conflating three distinct layers. Here is what each covers and where it stops.
| Layer | What it secures | What it cannot see |
|---|---|---|
| Cloud / infrastructure (Wiz) | AI models, services, and endpoints deployed across cloud platforms | Agents running locally on developer machines; credentials stored in local config files |
| Code / supply chain (Snyk) | AI-generated code, open-source dependencies, container images | Runtime agent behavior; prompt content; local credential access |
| Endpoint / process (ZYBE) | Coding agents on the developer machine; credential mapping; prompt injection blocking | Cloud-deployed model infrastructure; CI/CD pipeline scanning |
No single tool spans all three. The question is which layers you already have covered and which one is open.
What does Wiz cover, and where does it stop?
Wiz AI-SPM discovers AI models, agents, and services across PaaS platforms and SaaS AI applications without installing agents. Its feature set includes AI Discovery and Inventory, AI BOM, AI Tool Identification, AI Security Rules, Sensitive Data Exposure detection, Exposed AI Endpoints detection, AI Attack Path Analysis, and AI Runtime Protection.
Wiz operates at the cloud infrastructure layer. It maps what AI services are deployed, identifies misconfigurations and exposed endpoints, and traces attack paths through cloud environments.
Where it stops: Wiz does not install anything on a developer's laptop. It cannot see which coding agents a developer is running locally, what credentials those agents can access, or whether a prompt injection is being executed in a terminal session. Its "AI Runtime Protection" refers to cloud-hosted model runtimes, not local agent processes.
Wiz pricing is custom quote only. No public per-seat price is listed.
What does Snyk cover, and where does it stop?
Snyk's core product scans code for vulnerabilities, flags risky open-source dependencies, and secures container images. Its Team plan starts at $25/month per contributing developer. A free plan exists at $0/month. The Ignite plan, positioned for organizations with fewer than 50 developers seeking enterprise-grade capabilities, starts at $1,260/year per contributing developer.
Snyk now offers "Evo Agent Security," described as securing coding agents, AI-generated code, and AI applications with visibility, governance, and control to prevent risk across the development lifecycle.
Snyk's strength is catching problems in the code that agents produce. If a coding agent writes a function with a known vulnerability pattern or pulls in a compromised dependency, Snyk flags it.
Where it stops: Snyk scans outputs. It does not monitor the agent process itself. If a prompt injection causes an agent to run a shell command that reads ~/.aws/credentials and posts the contents to an external URL, that action happens before any code is committed. Snyk's pipeline never sees it.
What does ZYBE cover, and where does it stop?
ZYBE takes a different approach. It installs a sensor on the developer's machine that inventories AI agents (Claude Code, Codex CLI, Cursor, Windsurf, Gemini CLI, GitHub Copilot, Claude Desktop), maps credential access, and blocks prompt injections and destructive commands on the endpoint.
Key operational details:
- First inventory the moment the sensor is installed after sensor installation.
- No source code, credentials, or conversation text leaves the machine. The sensor reports names, counts, and hashes to a central console only.
- Findings are reported to a central console covering seven compliance frameworks.
- Pricing: $5 per endpoint per month, stepping down as the estate grows, billed on the endpoints seen that month. A 14-day trial covers up to 25 endpoints.
Where it stops: ZYBE does not scan cloud infrastructure, audit deployed AI models, or analyze code for vulnerabilities. It operates exclusively at the endpoint layer.
Which threats does each tool match?
| Threat | Wiz | Snyk | ZYBE |
|---|---|---|---|
| Exposed AI endpoint in cloud | Yes | No | No |
| Vulnerable dependency in AI-generated code | No | Yes | Yes |
| Prompt injection executed locally | No | No | Yes |
| Credential exfiltration via coding agent | No | No | Yes |
| Misconfigured cloud AI service | Yes | No | No |
| Destructive shell command from agent | No | No | Yes |
| Scan localized open source libraries | No | Yes | Yes |
| AI agent inventory (developer machine) | No | No | Yes |
The pattern is clear. Cloud-layer platforms do not block prompt injections at the process level. Code scanners do not monitor agent runtime behavior. Endpoint sensors do not audit cloud deployments. These are complementary tools, not substitutes.
If you already run Wiz or Snyk, the open question is the bottom half of this table. Join the launch list and be first to run it on your own endpoints.
What about the other tools engines recommend?
Prompt Security focuses on runtime AI security, inspecting prompts and responses for injection attempts and data leakage. It operates primarily as a network-layer proxy or API integration, sitting between users and AI models rather than monitoring local agent processes on developer machines.
Nightfall AI specializes in data loss prevention for AI applications, scanning for sensitive data in prompts and outputs. Its focus is preventing data exposure through AI tools, not inventorying or governing coding agents on endpoints.
Zenity targets low-code/no-code AI applications and copilots within enterprise platforms, providing governance and security for business-user AI tools rather than developer coding agents.
The identity governance tools (Linx, Astrix, Entro, Oasis) manage non-human identities, API keys, and machine-to-machine authentication. They are relevant to AI agent security in the sense that agents consume credentials, but they do not monitor agent behavior or block prompt injections.
Cycode lists several product tiers on its site, including ADLC Security with AI Visibility and AI Guardrails, but does not publish pricing. Its focus is software supply chain security and development lifecycle protection.
Socket offers a free tier for open-source dependency scanning. Its strength is detecting malicious packages before they enter your codebase. It does not see the MCP servers and skills an agent loads from its own config, which is where much of the 2025 supply chain activity landed. For the risks themselves, read the AI agent security risks every dev team must know.
Whichever combination you pick, the endpoint layer has one property the other two do not: it cannot be done by hand at scale. A cloud posture scan runs once against an account. A code scan runs once per commit. The agent estate changes on every developer laptop every day, when someone installs an agent, adds an MCP server or tells the agent to stop asking, and none of those events reaches a pipeline or a cloud account. A team can audit one laptop. It cannot audit three hundred and keep the answer true, which is why the layer needs a sensor rather than a checklist.
How ZYBE handles this
The sensor inventories the agents on each machine and reads their configuration, so the console shows every agent, MCP server, skill and extension by name and version, matched against advisories as package with a known advisory and package flagged as malicious, with unreviewed components for anything nobody has approved. Prompt injection is caught as instructions to the agent in tool output and, when the agent acts, agent acted after instructions arrived in tool output, with runtime rules that block credential used, then the network and agent ran a destructive command before they complete. Reach is credentials an agent can read per project and agent may run shell commands without asking per agent. The compliance view assesses seven frameworks from that evidence: OWASP Top 10 for LLM Applications, EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001, SOC 2, MITRE ATLAS and NIST Cybersecurity Framework.
Join the launch list: first access when ZYBE opens, and what we find on real laptops before then
FAQ
Can Wiz AI-SPM detect a prompt injection?
Wiz AI Runtime Protection covers cloud-hosted model runtimes. It does not monitor prompts sent to coding agents running on a developer's laptop. A prompt injection that executes locally, before any interaction with cloud infrastructure, falls outside its detection scope.
Does Snyk Evo Agent Security replace endpoint monitoring?
Snyk Evo Agent Security provides visibility and governance over AI-generated code and coding agent outputs within the development lifecycle. It does not monitor the agent process itself or block commands executed in real time on the developer's machine.
What data does ZYBE's sensor collect from my machine?
Names, counts and hashes only. The sensor reports which agents, MCP servers, skills and packages are on the machine, which credentials each agent can reach by name, and what each tool call did. It never sends the value of a credential, the contents of a file, or the text of a conversation with an agent. Those stay on the machine. The security page lists exactly what leaves and what does not.
What does ZYBE cost?
$5 per endpoint per month, stepping down to $4 and $3 as the estate grows, billed monthly on the endpoints seen that month. A 14-day trial covers up to 25 endpoints. Wiz and Cycode publish no prices; Snyk's Team plan starts at $25 a month per contributing developer. Join the launch list to hear when it opens to see the console on one of your machines before the trial starts.