Why traditional security fails against agentic AI
This non-deterministic behavior outpaces traditional security scanning, making AI Red Teaming the critical, dynamic validation layer for testing AI-native applications.
The shift to agentic AI creates three fundamental security gaps that traditional tools can’t address:
The velocity gap: Agentic AI moves fast, generating code and unpredictable behavior faster than humans can review.
The black box problem:
SAST tools detect known flaws in source code, but they can’t validate or secure the opaque decision-making of nondeterministic LLM-based applications.
DAST tools do not focus on new attack vectors inherent in LLM-based applications (e.g., data leakage through prompt injection)
New attack classes:
Prompt injection: Manipulating the model’s instructions to compromise behavior.
Sensitive data exfiltration: Tricking the model into revealing internal data or context.
Insecure outputs: Causing the AI to hallucinate or generate risky code or behavior.
Penetration testing is still required but not sufficient for AI-native applications since it is typically an exercise that runs on a schedule (e.g., once every six months). Red teaming uses AI to simulate real attack behavior to uncover the vulnerabilities that truly matter in production.
Introducing Snyk AI Red Teaming
To meet the non-deterministic challenges posed by AI-native apps, Snyk has introduced Snyk AI Red Teaming (now in experimental preview). This autonomous offensive tool simulates real-world adversarial attacks against live AI-native applications continuously. It moves offensive testing from an occasional, manual exercise to an always-on validation layer designed to build trust in your AI systems.
How Snyk AI Red Teaming works:
Modern AI-native systems are agentic, non-deterministic, and capable of chaining actions across APIs, databases, and user-facing flows. Scans or pen tests that are run on a set schedule (e.g., once every 6 months) require long wait times to discover new vulnerabilities.
Snyk AI Red Teaming simulates adaptive adversaries by continuously probing and testing the system, uncovering compound threats like prompt injection and data exfiltration, and proving impact with verifiable proof of exploit evidence so teams can prioritize remediation by real business risk.
This is done by autonomous agents that learn and adapt as they execute the following steps:
Reconnaissance: AI Red Teaming starts by probing the LLM-based application, attempting to jailbreak the model.
Contextual understanding: It interprets system prompts and identifies connections (e.g., Is it connected to a database? Does it leverage any MCP servers?)
Multi-stage exploits: Using the information it gathers, the agent executes targeted attacks, such as SQL injection, chaining each step to simulate a realistic adversary. For example, if it suspects that the app uses the LLM to execute SQL, it will try to run a SQL Injection attack. If it knows that the application has multiple users, it will try to extract information about other users.
Proof of exploit evidence: Every finding is documented with actionable evidence, not just potential vulnerabilities.
The AI Red Teaming CLI makes these advanced techniques accessible to developers: define targets, select threat types (data leakage, unauthorized code execution, prompt injection), run simulations, and get reproducible results for fast remediation. Designed to run in CI/CD or as one-off CLI tests, the system produces reproducible findings, clear severities, and the full conversation or payload that demonstrates the exploit, all safely executed within sandboxed environments.
Continuous security for everyone
By integrating red teaming into the development loop, Snyk transforms offensive testing from a rare, manual exercise into a continuous safeguard for AI-native applications.
Developers: The CLI provides fast, targeted testing of known AI endpoints with actionable fixes and reproducible evidence so vulnerabilities are caught before merge.
Security engineers: Autonomous red teams deliver continuous coverage, prioritized findings, and high-fidelity proofs that reduce false positives and scale triage.
Leaders & risk owners: Red Teaming turns abstract model risks into measurable business impact, enabling defensible decisions about guardrails, policy, and investments.
Red Teaming shifts security from an occasional audit to an always-on validation engine that keeps pace with agentic systems.
From findings to fixes: Time to go on offense
The future of red teaming doesn’t stop at detection; it drives remediation. It is being built to provide actionable fixes, from strengthening guardrails and improving prompts to automatically generating pull requests.
Snyk AI Red Taming complements our existing capabilities:
Static analysis for traditional code vulnerabilities.
SCA/DAST for dependencies and runtime checks.
AI-specific scanning (via AI-BOM and MCP Scan) for deep, model-aware coverage.
This integration brings depth, breadth, and automation into one continuous, developer-first security loop, where intelligent systems test, learn, and protect other intelligent systems.
Shape the future of AI security with Evo by Snyk
This is a generational moment in security, and as AI becomes the foundation of modern software, the way we test, secure, and defend it must evolve just as quickly.
AI Red Teaming is a step towards this vision, giving teams the ability to test AI systems in real time, surface actionable findings with proof of exploit, and build a culture of always-on, intelligent offensive security.
As we continue with Evo by Snyk’s agentic security orchestration system, the power of AI Red Teaming Agent will be T one component of this autonomous framework, continuously probing AI-native applications with multi-stage, context-aware attack chains. By integrating findings across discovery, governance, and protection tools, Evo will be able to transform security from a periodic checkpoint into a seamless, always-on part of development workflows. Actionable results feed into automated policies, threat modeling, and remediation pipelines, enabling developers and security engineers to validate and fix vulnerabilities in real time.
While AI agents can learn, adapt, and operate autonomously, Evo ensures that teams know how their systems respond before attackers do, establishing a new standard for continuous, intelligent, and scalable AI security.
Want to get started with AI Red Teaming today?
Try AI Red Teaming: Available now in experimental preview for anyone with a Snyk account. Explore red teaming and other AI security incubations directly from the CLI.
Join the Evo by Snyk Design Partner Program: Apply today to gain early access to the full Evo UI experience, where red teaming connects seamlessly with discovery, governance, and protection capabilities in the unified Evo by Snyk agentic orchestration system.
