Straiker is an Agentic AI Security vendor based in Sunnyvale, USA. Its flagship product is Straiker platform (Discover AI, Ascend AI red teaming, Defend AI runtime). Against the OWASP LLM Top 10 (2025) we score it Covered on 5 of 10 risks, Partial on 5, with 6 MITRE ATLAS techniques mapped as of . It's independent.
- 5/10
- Risks covered
- 5
- Partial
- 6
- ATLAS techniques
- $85M
- Funding
- Active
- Status
At a glance
- Category
- Agentic AI Security
- Headquarters
- Sunnyvale, USA
- Founded
- 2024
- Employees
- 11-50
- Flagship product
- Straiker platform (Discover AI, Ascend AI red teaming, Defend AI runtime)
- Deployment
- SaaS; inline runtime protection plus API/agent integration
- Best for
- Enterprises deploying autonomous AI agents and MCP servers that need agent discovery, adversarial testing and runtime blocking
- Funding to date
- $85M
- Last round
- Series A ($64M) · 2026-06
- Status
- Active
Facts as of .
What does Straiker do?
Built agent-first — discovers the AI agents running in your company, tests them adversarially, and defends them live. It ships as SaaS; inline runtime protection plus API/agent integration, and it's built for enterprises deploying autonomous AI agents and MCP servers that need agent discovery, adversarial testing and runtime blocking. It's a weaker fit for teams securing classic predictive ML or needing model-file supply-chain scanning as the priority.
Which OWASP LLM Top 10 risks does Straiker cover?
Straiker's strongest verdict is LLM01Prompt Injection. The two tables below come from our editorial read of Straiker's public documentation as of — it's what the docs support, not a hands-on lab test — and this row also sits in the full matrix across all 21 vendors.
| OWASP LLM Risk | What it is | Straiker coverage | How it's addressed | Source |
|---|---|---|---|---|
| LLM01 Prompt Injection | User or hidden input overrides the model's rules or intended behavior. | Covered | Ascend AI tests prompt injection; Defend AI blocks it inline at runtime. | https://www.straiker.ai/ |
| LLM02 Sensitive Information Disclosure | The model leaks secrets, personal data, or proprietary content in its output. | Covered | Defend AI detects and blocks data exfiltration at runtime. | https://www.straiker.ai/ |
| LLM03 Supply Chain | Compromised models, datasets, plugins, or dependencies add risk before runtime. | Partial | Discover AI maps MCP servers and flags dangerous/misconfigured MCP tools; not model-file supply-chain scanning. | https://www.straiker.ai/ |
| LLM04 Data and Model Poisoning | Tampered training or fine-tuning data corrupts how the model behaves. | Partial | Defend AI blocks agent memory poisoning at runtime; training-data poisoning not addressed. | https://www.straiker.ai/ |
| LLM05 Improper Output Handling | Downstream systems trust model output without checking it, enabling injection or code execution. | Partial | Defend AI's detection engine is documented as collecting 'full-chain telemetry across input, output, conversation, RAG content, attachments, tool calls, MCP traffic, and session behavior,' indicating output inspection is part of runtime guardrails, though not framed as dedicated output-handling/sanitization control. | https://www.straiker.ai/products/defend-ai |
| LLM06 Excessive Agency | An agent holds more permissions, tools, or autonomy than the task needs. | Covered | Ascend/Defend address goal hijacking, tool misuse and inter-agent manipulation. | https://www.straiker.ai/ |
| LLM07 System Prompt Leakage | The system prompt or the secrets inside it get exposed to users. | Covered | Runtime AI guardrails documentation explicitly lists 'system prompt leaks' alongside prompt injection and data exfiltration as risks the platform addresses. | https://www.straiker.ai/solution/guardrails |
| LLM08 Vector and Embedding Weaknesses | Flaws in RAG stores let attackers poison, extract, or infer data. | Partial | Defend AI's multi-layer telemetry collection explicitly includes monitoring of RAG content and vector components as part of its runtime coverage, though not a dedicated embedding-security control. | https://www.straiker.ai/products/defend-ai |
| LLM09 Misinformation | The model produces false or fabricated content that users act on. | Covered | Defend AI's detection engine runs specialized detectors for hallucination alongside prompt injection, tool misuse, and code-injection patterns as a core guardrail category. | https://www.straiker.ai/products/defend-ai |
| LLM10 Unbounded Consumption | Uncontrolled requests drive cost, denial of service, or model extraction. | Partial | Defend AI blocks resource exploitation and identity abuse at runtime. | https://www.straiker.ai/ |
| ATLAS Technique (ID) | Tactic | Straiker coverage | Notes | Source |
|---|---|---|---|---|
| LLM Prompt Injection (AML.T0051) | Initial Access / Execution | Covered | Tested by Ascend AI and blocked inline by Defend AI. | https://www.straiker.ai/ |
| LLM Data Leakage (AML.T0057) | Exfiltration | Covered | Runtime detection/blocking of silent data exfiltration by agents. | https://www.straiker.ai/ |
| LLM Plugin Compromise (AML.T0053) | Execution / Privilege Escalation | Partial | Addresses tool/plugin misuse and dangerous MCP tools in agent workflows. | https://www.straiker.ai/ |
| Craft Adversarial Data (AML.T0043) | ML Attack Staging | Covered | Ascend AI adversarial testing engine generates attacks against agents. | https://www.straiker.ai/ |
| LLM Jailbreak (AML.T0054) | Privilege Escalation / Defense Evasion | Partial | Adversarial testing covers goal hijacking and guardrail bypass. | https://www.straiker.ai/ |
| External Harms (AML.T0048) | Impact | Partial | Runtime controls limit downstream harm from compromised agents (RCE, exfiltration). | https://www.straiker.ai/ |
Is Straiker independent, and how is it funded?
Straiker is an independent company as of . It has raised $85M to date, most recently a Series A ($64M) dated 2026-06. Lead investors: Marathon, Citi Ventures, Workday Ventures.
Straiker alternatives
The closest alternatives we track in Agentic AI Security are Akto, Vijil. On the open-source side, Agentic Security covers similar ground.
Frequently asked questions
What is Straiker used for?
Straiker is an Agentic AI Security vendor. Its flagship product is Straiker platform (Discover AI, Ascend AI red teaming, Defend AI runtime). It ships as SaaS; inline runtime protection plus API/agent integration, and it's built for enterprises deploying autonomous AI agents and MCP servers that need agent discovery, adversarial testing and runtime blocking.
Is Straiker independent or acquired?
Straiker is an independent company as of 2026-07-13 and has not been acquired.
How many OWASP LLM Top 10 risks does Straiker cover?
We score Straiker as Covered on 5 of the ten OWASP LLM Top 10 (2025) risks, with 5 more Partial, as of 2026-07-13. The table on this page breaks down every risk, including the ones marked Not covered or Unverified.
How is Straiker deployed?
Straiker ships as SaaS; inline runtime protection plus API/agent integration.
What are the best alternatives to Straiker?
The closest Agentic AI Security alternatives llmthreat tracks are Akto, Vijil. On the open-source side, Agentic Security covers similar ground.