Agentic AI Security: 3 Vendors and 1 Tools Compared for 2026
Agentic AI Security covers controls for AI that acts — tool-calling agents and MCP traffic — where excessive agency turns a bad prompt into a bad action. It maps most closely to OWASP LLM risks LLM06 Excessive Agency, LLM05 Improper Output Handling, LLM01 Prompt Injection. 3 vendors and 1 open-source tools sit in this category, scored as of .
3 of 3 Agentic AI Security vendors score Covered for LLM06 Excessive Agency, as of . See the whole market in the coverage matrix.
Vendors
Ranked by llmthreat's editorial coverage score, as of 2026-07-13 — public documentation, not a third-party audit.
| Vendor | Covered | Partial | Status | Funding | Best for |
|---|---|---|---|---|---|
| Straiker | 5/10 | 5 | Active | $85M | Enterprises deploying autonomous AI agents and MCP servers that need agent discovery, adversarial testing and runtime blocking |
| Vijil | 5/10 | 4 | Active | $23M | Teams building and operating AI agents that need context-specific red teaming plus low-latency runtime guardrails and hardened base models across the agent stack (LLM, tools, MCP, delegated agents) |
| Akto | 3/10 | 4 | Active | $4.5M | Teams that already need API security and want unified discovery plus testing of AI agents, MCP servers and LLM endpoints |
Open-source tools
| Tool | License | Language | Stars | Last push | Status | Primary use |
|---|---|---|---|---|---|---|
| Agentic Security | Apache-2.0 | Python | 1,925 | 2026-06-23 | Active | Agentic LLM vulnerability scanner / AI red-teaming kit for testing autonomous agent pipelines against security threats |
How to choose a Agentic AI Security product
In this category the product differences aren't about "does it stop prompt injection" — almost all claim that. They're about where in the agent's tool-calling loop the control actually sits, whether it can see MCP-specific protocol attacks (not just chat-level jailbreaks), and how it handles the human-approval bottleneck once an agent is making hundreds of tool calls an hour.
- 01 Enforcement point: gateway/proxy vs in-process SDK vs generic API gateway
- A dedicated MCP gateway (e.g. patterns like Microsoft's mcp-gateway, agentgateway) sits between agent and tool server and is protocol-aware — it can filter tool discovery per-caller and inspect tool-call arguments. In-process SDK middleware runs inside the agent's own code path with zero extra network hop but must be wired into every framework separately and is bypassed if any code path calls the MCP server directly. A generic API/network gateway proxies the traffic but has no concept of a 'tool call' vs any other JSON body, so it can rate-limit and log but can't enforce tool-level policy.
- 02 Detection-only vs inline blocking
- Some products just flag or log agentic actions after the fact (detection); others sit inline and block the tool call before it executes (prevention). Inline blocking actually stops the exfil or the destructive call, but it adds latency to every single tool invocation and a false positive breaks the agent's workflow mid-task — vendors like Straiker publicize sub-300ms and sub-130ms detection budgets specifically because agent loops make many calls per task and latency compounds. Detection-only preserves speed but the action already happened by the time you're alerted.
- 03 MCP-specific protocol coverage vs repurposed LLM guardrails
- OWASP's MCP Top 10 (beta) catalogs protocol-layer attacks like tool poisoning (MCP03:2025), tool shadowing, and 'rug-pull' server updates (a tool's description or behavior changes after the agent already trusts it) — these live in tool metadata and server responses, not in the user prompt. A guardrail built for chat (prompt injection, jailbreak, PII in output) may never parse tool descriptions, tool schemas, or the tool's returned payload re-entering context, so it's structurally blind to this attack class even if it scores well on jailbreak benchmarks.
- 04 Identity model: agent bound to human identity vs shared service credentials
- Products built around non-human identity (NHI) bind each agent session to the human or workflow it acts for, scope credentials per tool, and produce an audit trail that attributes the action to a person. This requires IdP integration (Okta/Entra/Keycloak) and identity lifecycle work. The alternative — one shared API key or service account for the agent — is fast to set up but means a single prompt injection inherits the full blast radius of whatever that service account can touch, with no way to trace which human session triggered it.
- 05 Human-in-the-loop design: blanket approval vs risk-tiered escalation
- Gating every sensitive action behind synchronous human approval is the safest control on paper, but at agent scale (hundreds of tool calls/hour) it produces the same confirmation fatigue documented in SOC alert-fatigue research — reviewers start rubber-stamping. Confidence- or risk-tiered escalation (auto-approve low-risk, queue only high-risk/high-blast-radius actions) scales better but shifts the risk onto the product's risk model: if the tiering is miscalibrated, a genuinely dangerous action gets auto-approved and never reaches a human.
Most vendors demo detection against static jailbreak/prompt-injection benchmarks run through a plain chat completions endpoint — not against a live multi-turn agent loop calling a real MCP server. Ask specifically whether the product inspects tool descriptions, tool-call arguments, and tool *output* re-entering context (where tool poisoning, rug-pull updates, and indirect prompt injection actually live per OWASP's MCP Top 10 and CoSAI's MCP threat whitepaper), and whether it re-evaluates on every step of a session or only the first prompt — a guardrail that checks turn one misses drift accumulated over a 40-step agent run.
Frequently asked questions
What is Agentic AI Security?
Controls for AI that acts — tool-calling agents and MCP traffic — where excessive agency turns a bad prompt into a bad action.
Which vendors lead Agentic AI Security coverage?
Straiker, Vijil, Akto cover the most OWASP LLM Top 10 risks in Agentic AI Security, by llmthreat's scoring, as of 2026-07-13. See the ranked table above.
What is the best Agentic AI Security tool for excessive agency?
Straiker leads Agentic AI Security for Excessive Agency, by llmthreat's coverage scoring: Enterprises deploying autonomous AI agents and MCP servers that need agent discovery, adversarial testing and runtime blocking. Compare the full ranked list above.