2026 WINNER · CYBERSECURITY STARS AWARDS
Straiker Ascend AI · Continuous AI Red Teaming Platform
Best AI Security Testing Platform
Overview
Straiker is the agentic security company. Its AI adversarial testing product, Straiker Ascend AI, continuously red-teams AI agents to expose real attack paths before they reach production. What separates Straiker from every other testing vendor starts with access: Straiker holds paid pre-release testing engagements with frontier AI labs, meaning Straiker's researchers are stress-testing novel agentic attack patterns before those patterns are publicly known or in the hands of adversaries. That intelligence, combined with millions of synthetic data points, feeds a continuously improving attack engine that covers 67+ controls across 10+ attack categories, giving Ascend AI the highest attack simulation success rate in the industry. The result is adversarial testing that runs on how agents actually get compromised in production, not on theoretical playbooks or last year's threat models. Enterprises no longer have to choose between shipping AI fast and knowing it's secure before it ships.
Key Capabilities
Straiker Ascend AI continuously red-teams AI agents to expose real attack paths before they reach production. Unlike one-off penetration tests, Ascend AI runs as a continuous testing surface that re-tests as agents evolve, validates that fixes hold, and exercises new capabilities as they ship. The engine operates in three phases: app context ingestion (system prompt, architecture, tooling), automated reconnaissance across MCP servers, tools, databases, and infrastructure, and then active attack execution.
Core capabilities include:
- Automated attack simulation across 67+ controls in 10+ categories, covering data leaks (PII, corp confidential), LLM evasion, identity and excessive agency, application grounding, safety (cybercrime, hate speech, bio weapon), and agentic manipulation
- Multi-strategy attack execution: Fuzzing and encoding, role playing, typoglycemia, foreign language injection, and excessive tool agency attacks, each designed to probe failure modes that simple red-team scripts miss
- Adaptive prompting across all LLM types: Ascend AI deploys unaligned LLMs, fine-tuned attack LLMs, and frontier LLMs as attacker agents, adapting prompting strategies based on target agent behavior in real time
- Full agentic stack coverage: Attacks target the full four-layer architecture, from the application and orchestration layer through the model, tools, MCP servers, and data connectors
- Production-grade cyber ranges: Straiker builds and maintains realistic simulation environments, complete with live connectors, real tools, and real data flows, so enterprises can test agents against conditions they will actually face rather than synthetic sandboxes
- Saige conversational red-team interface: Enables security and product teams to run targeted adversarial tests through a guided, conversational interface without requiring offensive security expertise
How we are different
Straiker holds paid pre-release testing engagements with frontier AI labs. That access means Straiker's attack engine is trained on adversarial scenarios before they are publicly known, before they appear in research papers, and before adversaries operationalize them. Combined with millions of synthetic data points, this creates a data moat that no competitor can close by simply building a better testing interface.
- Highest attack success rate in the industry. Ascend AI's attack engine surfaces vulnerabilities that competitors miss because it is fueled by real-world agentic attack patterns, not theoretical playbooks. That claim is backed by published results across 67+ controls.
- Fine-tuned attack LLMs, not just off-the-shelf models. Straiker's attack agents include purpose-built, fine-tuned LLMs alongside frontier and unaligned models, giving the engine adversarial depth and flexibility that generic red-team tools cannot replicate.
- Continuous testing, not a quarterly event. Ascend AI re-tests as agents evolve, validates that remediations hold, and exercises new capabilities as they ship. Security posture does not degrade between assessment cycles.
- Findings that drive real outcomes. A popular home appliance company ran Ascend AI against their Zoom agent, identified critical safety issues, and subsequently rebuilt the agent entirely on Salesforce based on those findings. A cybersecurity company committed to adversarially test all new AI products before release using Straiker, and Ascend AI's red-team results now serve as primary evidence in their responses to third-party risk questionnaires. These are not demo findings; they are architectural decisions and compliance artifacts driven by Straiker's output.
- Attack intelligence feeds runtime defense. Attack patterns discovered through Ascend AI continuously sharpen Straiker Defend AI's detection engine, creating a closed-loop system where offensive findings strengthen defensive coverage over time.
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