What is AI Red Teaming: Examples, Tools & Best Practices

Artificial intelligence is rapidly becoming a core execution layer inside the enterprise. From copilots and chatbots to autonomous agents that plan, decide, and act across systems, AI is no longer experimental. According to McKinsey’s 2025 survey, 88% of organizations now use AI in at least one business function, up from 78% a year earlier. 

More notably, 62% are already experimenting with autonomous AI agents, and nearly a quarter have begun scaling agentic systems in production. These systems are delivering material gains. Early adopters report 40 to 50% productivity improvements in functions like IT and HR, and JPMorgan has publicly credited AI agents with saving more than 360,000 employee hours in a single year.

But as autonomy increases, so does risk. AI incidents are rising sharply, increasing by roughly 50% in just 6 months according to OECD tracking. Real failures illustrate why. 

Coding agents have deleted production databases due to misinterpreted objectives. Enterprise chatbots have been prompt injected into leaking confidential data. Security researchers have demonstrated how a single malicious email can hijack a corporate AI assistant and silently alter downstream decisions. These are not theoretical risks. They are operational failures emerging from systems that act without deterministic control.

This is creating a new mandate for security leaders. Regulators are already responding. The EU AI Act requires rigorous adversarial testing for high risk AI systems, and recent US executive actions mandate red teaming and disclosure of results for advanced models. For CISOs and CEOs, the implication is clear. You cannot scale AI agents safely without systematically trying to break them first.

This article explains what AI red teaming is and why it has become a foundational control for modern AI security. It examines how red teaming exposes failures such as prompt injection, tool misuse, data exfiltration, and unsafe autonomous behavior. The blog outlines the core objectives, methods, and challenges of testing non deterministic, multi-agent systems, and we review the emerging landscape of AI red teaming tools from open frameworks to enterprise platforms. How Levo helps organizations operationalize AI red teaming as part of a broader total AI security strategy that combines adversarial testing with continuous ai monitoring and enforcement.

AI red teaming is no longer a research exercise. It is how organizations protect revenue, data, and trust in an AI driven enterprise.

What is AI Red Teaming?

AI red teaming is the practice of proactively stress testing AI systems to uncover weaknesses before real adversaries exploit them. It is the AI equivalent of a penetration test, designed for a very different attack surface. A red team, whether internal or external, deliberately behaves like a malicious actor and attempts to break AI models and agents through adversarial inputs, misuse scenarios, and deceptive interactions. The objective is to surface unsafe behaviors, security gaps, and failure modes that could lead to real world harm.

Unlike traditional security testing, AI red teaming focuses less on software bugs and more on behavior. AI systems can fail without crashing. They may follow unauthorized instructions, leak sensitive information, misuse connected tools, or make unsafe decisions while appearing to function normally. Red teams probe these risks by simulating realistic threat scenarios such as prompt manipulation, indirect instruction injection, data exfiltration attempts, or abuse of tool access and permissions.

AI red teaming is a structured adversarial testing effort designed to identify flaws and vulnerabilities in AI systems. In practice, this means systematically challenging the model with carefully crafted inputs and multi step interactions that mirror how attackers actually operate. If an AI agent can be coerced into violating policy, bypassing safeguards, or acting outside its intended scope, that behavior is treated as a security finding that must be mitigated.

For CISOs and CTOs, AI red teaming can be thought of as ethical hacking for autonomous systems. Just as organizations test networks and applications by trying to break them, AI red teaming intentionally pushes models and agents into edge cases and hostile conditions. The goal is not academic safety validation, but operational risk reduction. Done correctly, AI red teaming prevents compliance failures, reputational damage, and financial loss by ensuring AI systems behave safely and predictably when exposed to real adversaries, not just ideal users.

Why is AI Red Teaming Important for AI Security?

AI red teaming matters because autonomous AI changes the risk model. Unlike traditional software, AI agents interpret goals and decide actions across tools, data, and systems. That autonomy creates value, but it also creates new failure modes. When an agent is misled, it can act with the authority of a trusted employee, executing transactions, accessing sensitive data, or modifying systems without explicit instructions.

This risk is scaling fast. Over 60% of large enterprises are already piloting or deploying AI agents, and nearly a quarter are running them in production. At the same time, tracked AI incidents have increased by roughly 50% year over year. Autonomy is expanding faster than control, creating material business exposure.

Traditional security controls do not address these risks. Prompt injection, indirect instruction attacks, and tool misuse are behavioral failures, not code defects. Static scans, role based access reviews, and one time testing cannot detect how an AI will behave when inputs, context, or goals change. An agent may pass validation on day one and fail catastrophically later under a novel prompt or data condition.

AI red teaming closes this gap by stress testing agents under adversarial conditions before they are trusted with real authority. It shows whether an agent can be manipulated into leaking data, bypassing safeguards, or taking unsafe actions. Without this testing, organizations compensate by restricting agent permissions or adding heavy human oversight, which erodes the 30 to 50% productivity gains AI is meant to deliver.

Red teaming enables safer autonomy. By identifying and fixing behavioral weaknesses early, organizations can confidently delegate higher value tasks to AI without constant supervision. It also provides defensible evidence of risk management for regulators, auditors, and boards. 

In short, AI red teaming is the control plane that makes AI at scale viable. It protects business value by allowing innovation to move fast without accepting uncontrolled operational, financial, or reputational risk.

Key Objectives of AI Red Teaming

AI red teaming is about validating control, not just capability. Its objective is to ensure AI systems behave safely, predictably, and within intent when exposed to real adversarial pressure. For security and business leaders, these objectives directly map to risk reduction, trust, and scalable adoption.

• Identify and mitigate AI specific vulnerabilities: The primary goal is to uncover weaknesses in AI behavior that traditional security testing misses. This includes susceptibility to prompt injection, data leakage, unsafe reasoning, or misuse of tools and APIs. By exposing these flaws early, red teaming prevents high impact incidents and costly downstream remediation.
• Ensure AI decisions stay within intent: Red teaming validates that AI agents do not exceed the roles, permissions, or authority they are given. An agent should not execute unauthorized actions, access restricted data, or make decisions outside defined boundaries. Testing decision logic and tool usage under adversarial conditions confirms the agent cannot go rogue when pressured.
• Validate model robustness and integrity: Another core objective is to assess how resilient the model is to manipulation. Red teaming tests whether the AI can be jailbroken, coerced into ignoring system instructions, forced to hallucinate, or induced to reveal sensitive or proprietary information. This establishes confidence that the model remains aligned and controlled, even when inputs are intentionally malicious.
• Expose risks in multi step and multi agent workflows: AI failures often emerge from interactions, not components. Red teaming stress tests chained workflows where agents, orchestration layers, models, and APIs interact. The goal is to surface emergent risks such as unintended data sharing, unsafe decision propagation, or privilege misuse that only appear across the full execution path.
• Protect sensitive data and identities: Many AI agents operate with access to confidential data and delegated credentials. Red teaming verifies that data cannot be exfiltrated through clever prompting and that identity boundaries hold under attack. This includes testing for privilege escalation, token misuse, and cross tenant access failures that could lead to serious breaches.
• Generate evidence and assurance: Beyond discovery, red teaming produces proof. Each test creates documented, auditable evidence of how the AI behaves under attack and how issues are mitigated. This supports internal governance, board level assurance, and regulatory scrutiny, while enabling continuous improvement as controls mature.

The objective of AI red teaming is not to break AI for its own sake, but to make AI safe, controllable, and trustworthy. By systematically attacking AI systems before real adversaries do, organizations can deploy autonomous AI at scale with confidence, discipline, and defensible risk posture.

How AI Red Teaming Works

AI red teaming evaluates AI systems the way real users and adversaries interact with them: end to end, in context, and over time. Instead of testing isolated inputs or static behaviors, it stress tests the full decision loop of models and agents to expose failures that emerge only during real execution.

• Test the full AI system, not just the model: Red teaming starts by modeling the complete AI architecture. This includes the foundation model, agent frameworks, orchestration layers, memory stores, tools, APIs, databases, and user interfaces. The system is tested in an environment that mirrors production behavior, often with instrumentation to observe decisions, tool calls, and data access. The objective is to validate how the AI behaves in context, not in isolation.
• Simulate realistic adversarial scenarios: Red teamers design attack scenarios that reflect how AI is actually abused. This can range from direct prompt injection to subtle, multi turn manipulation. Inputs may appear benign at first and gradually introduce malicious instructions, poisoned data, or misleading context. Testers may also seed the environment with malicious documents or crafted tool responses to see whether the AI can be coerced into unsafe behavior.
• Exercise chained and multi step decision flows: Most AI failures occur across sequences, not single actions. Red teaming follows the agent through full reasoning and execution chains, including planning, tool invocation, data retrieval, and follow on decisions. At each step, testers attempt to interfere or redirect behavior. This exposes vulnerabilities that only appear when multiple actions are composed, such as unsafe decisions triggered by earlier context.
• Use goal driven testing, not just scripted inputs: Rather than hardcoding every interaction, red teaming often sets high level goals and allows the agent to plan its own path. Testers then observe how the agent reasons and intervene at critical points with conflicting signals, corrupted data, or misleading errors. This approach reflects real world usage, where AI behavior cannot be exhaustively predicted or scripted.
• Monitor, trace, and analyze behavior: Throughout testing, detailed telemetry is collected across prompts, model outputs, tool calls, and system responses. Because AI behavior is non deterministic, scenarios are often replayed multiple times to assess consistency and risk. Modern red teaming emphasizes traceability, producing end to end execution logs that clearly show where and how controls failed.
• Produce actionable findings and iterate: The output of red teaming is a set of concrete findings with evidence, impact, and severity. These insights feed directly into remediation, such as tightening prompts, reducing permissions, adding guardrails, or modifying workflows. The process is iterative and increasingly continuous, evolving from a one time exercise into an ongoing control.

AI red teaming works by attacking AI systems as they actually operate in production, across components, decisions, and time. This combination of realism and rigor is what makes red teaming uniquely effective at uncovering AI specific risks before they turn into business incidents.

Examples of AI Red Teaming 

AI red teaming becomes concrete when you examine how real attacks manifest against models and agents in practice. These are not theoretical edge cases. They are failure modes already observed in enterprise deployments and public incidents.

• Prompt Injection (Direct): This is the most well known attack class. A tester provides an input designed to override the AI’s system instructions, such as asking it to ignore policies or reveal restricted information. Vulnerable systems comply when guardrails are weak or phrasing is subtle. Red teaming systematically tests direct prompt injections across dozens of variations to ensure safety controls hold under pressure.
• Indirect Prompt Injection: In indirect attacks, malicious instructions are embedded in content the AI consumes rather than in the user’s query. This could be a document, email, web page, or database record that the agent is asked to summarize or analyze. The AI unknowingly executes hidden instructions found in the source material, leading to data exfiltration or policy violations. Red teaming validates whether agents can safely handle untrusted context without blindly following embedded commands.
• Memory Poisoning: Agents that retain state or long term memory introduce a new attack surface. Red teamers attempt to inject malicious instructions into stored memory, profiles, or cached variables. The risk emerges later when the agent recalls that memory and executes unintended actions. These attacks are particularly dangerous because they are delayed, persistent, and difficult to trace without proper testing.
• Tool Misuse and API Exploitation: Agents with access to tools such as code execution, file systems, or APIs can be manipulated into misusing those capabilities. Red teaming tests whether agents can be coerced into running unsafe commands, calling sensitive endpoints, or importing insecure dependencies. This often involves hiding instructions inside files, error messages, or tool outputs that the agent treats as trusted context.
• Identity and Permission Abuse: AI agents frequently operate using delegated machine identities and tokens. Red teaming probes whether those identities are overly privileged or can be abused. Tests include simulating privilege escalation, impersonation of trusted agents, or bypassing approval workflows. The objective is to ensure agents cannot be tricked into acting as super users or violating separation of duties.
• Data Leakage and Social Engineering: Red teamers attempt to extract sensitive information by manipulating how questions are framed. This includes prompting the AI to explain its rules, recall confidential context, or reveal internal instructions. They also test whether the AI can be abused to generate phishing content, malware, or other harmful outputs through indirect requests. These scenarios validate privacy, confidentiality, and policy enforcement.

Each example represents a distinct failure mode that traditional security testing does not cover. The value of AI red teaming lies in surfacing these weaknesses before deployment, under controlled conditions. By repeatedly simulating real world abuse patterns, organizations can harden AI systems, reduce blast radius, and deploy autonomous agents with far greater confidence.

AI Red Teaming Methods and Process

Effective AI red teaming is not ad hoc experimentation. It follows a structured, repeatable process that applies multiple adversarial methods to test how AI systems behave under real world attack conditions. Each method targets a different risk class, and together they provide comprehensive coverage across models, agents, tools, and workflows.

AI Red Teaming methods ensure it is implemented in systematic, measurable, and aligned with enterprise risk objectives. Rather than testing isolated behaviors, the process validates that AI systems remain secure, controllable, and trustworthy across real world conditions and complex interactions.

• Prompt Injection Testing: Red teams craft adversarial prompts designed to override system instructions or manipulate agent behavior. These tests validate whether the AI can be coerced into ignoring policies, leaking data, or executing unauthorized actions through semantic manipulation. Any successful override represents a critical control failure that must be addressed.
• Privilege Escalation Simulation: These tests examine whether the AI can be abused to combine or misuse permissions. Red teamers simulate scenarios where delegated tokens, API keys, or agent roles interact in unintended ways. The objective is to ensure the AI cannot be used as a path to elevated access, unauthorized transactions, or administrative control.
• Sensitive Data Leakage Testing: Red teams evaluate whether private, regulated, or proprietary data can be exposed through AI outputs, memory, or tool usage. This includes indirect extraction attempts and roundabout queries designed to bypass safeguards. The goal is to confirm that sensitive data remains protected even under adversarial questioning.
• Rate Limiting and Abuse Stress Testing: Agents and their integrated APIs are subjected to high volume and repetitive requests to simulate scraping, denial of service, or brute force style abuse. These tests validate resilience under load and ensure that volume based attacks cannot trigger cascading failures, data leakage, or unstable behavior.
• Input Fuzzing: Automated fuzzing generates large volumes of malformed, obfuscated, or creatively phrased inputs to probe the AI’s parsing and interpretation boundaries. Natural language fuzzing helps uncover brittle logic where unusual phrasing, character substitutions, or language shifts bypass validation and safety checks.
• Multi Agent Workflow Simulation: For systems built on agent chains, red teams test adversarial scenarios across the entire workflow. This includes poisoning inputs between agents, introducing malicious intermediaries, or exploiting hidden dependencies. The objective is to surface emergent risks where individually safe agents produce unsafe outcomes when combined.‍
• Process and Outcomes: A typical red teaming engagement begins by defining scope and risk priorities, then applies a combination of these methods in a controlled environment. Findings are documented with transcripts, logs, and severity ratings, enabling remediation and retesting. When run continuously, this process becomes a feedback loop that hardens AI systems over time.

Challenges of AI Red Teaming

AI red teaming is harder than traditional security testing because AI systems behave differently from deterministic software. The core challenges are structural, not procedural. These constraints make AI red teaming complex, but unavoidable. As AI systems gain autonomy, untested edge cases become enterprise scale risks rather than theoretical flaws.

• Non Deterministic Behavior: The same input can produce different outcomes across runs. Vulnerabilities may surface intermittently, requiring repeated testing and statistical validation rather than one time reproduction.
• Language as the Attack Surface: Natural language is the primary exploit vector. Attackers can manipulate phrasing, tone, context, languages, or multimodal inputs to bypass controls. This semantic attack space is effectively unbounded and difficult to systematically cover.
• Complex Agent Chains: AI systems span multiple agents, models, tools, memory layers, and APIs. Failures often emerge only across chained interactions. Root cause analysis is difficult due to limited visibility and emergent behavior.
• Inadequate Legacy Tooling: Traditional scanners and pentest tools were built for structured inputs and deterministic paths. They miss AI specific failures such as prompt injection, tool misuse, and behavioral drift, forcing teams to adopt new platforms or custom frameworks.
• Rapid System Change: Models, prompts, tools, and agent capabilities evolve frequently. Point in time testing becomes obsolete quickly, making continuous red teaming a necessity rather than a best practice.‍
• Limited Interpretability: AI failures rarely map cleanly to a fix. Mitigations rely on guardrails, prompt redesign, or retraining, none of which provide deterministic guarantees. Some issues require ongoing detection rather than permanent resolution.

Benefits of AI Red Teaming

When executed well, AI red teaming shifts security from a constraint to a business enabler. The benefits extend beyond risk reduction into speed, trust, and operational resilience.

• Preventing High Impact Incidents: Red teaming exposes failures before they reach production. This avoids costly outcomes such as data leaks, fraudulent actions, regulatory penalties, and brand damage. The cost of prevention is materially lower than post incident remediation.
• Faster and Safer AI Deployment: Tested systems earn trust. When risk teams have evidence that AI agents behave correctly under attack, approvals accelerate. Red teaming converts unknown risks into managed ones, allowing organizations to ship AI capabilities without slowing innovation.
• Focus on Real, Exploitable Risk: Unlike broad scanning, red teaming produces proof backed findings. Security teams address validated failure modes rather than theoretical issues, improving remediation efficiency and reducing wasted effort.
• Improved Reliability and Alignment: Adversarial testing surfaces logic errors, brittleness, and misalignment issues alongside security flaws. Fixing these improves overall system reliability, decision quality, and user experience.
• Audit Readiness and Regulatory Confidence: Red teaming generates defensible evidence of due diligence. Logs, transcripts, and findings support regulatory reviews and customer audits, reducing friction in compliance heavy industries.
• Stronger Trust with Stakeholders: Demonstrating adversarial testing builds confidence with customers, partners, boards, and regulators. It signals responsible AI governance and differentiates AI offerings in risk sensitive markets.
• Continuous Learning and Resilience: Each testing cycle strengthens both systems and teams. Organizations develop institutional knowledge of AI failure modes, leading to more resilient agents and more security aware AI development over time.

AI red teaming reduces downside risk while unlocking upside value. It enables faster delivery, stronger assurance, and sustained trust as AI systems scale in autonomy and impact.

Best Practices for AI Red Teaming

A successful AI red teaming program is deliberate, continuous, and tightly integrated with how AI systems are built and deployed. Effective AI red teaming is not a single activity but an ongoing discipline: scoped clearly, embedded early, tested realistically, and improved continuously.

The following practices help teams move from adhoc testing to sustained risk control.

• Define Clear Objectives and Scope: Be explicit about what you are testing and why. Identify high impact systems first and document the failure modes you want to validate (such as, data leakage, prompt injection, privilege misuse). A clear scope keeps testing focused and actionable.
• Build a Multidisciplinary Team: AI red teaming requires security expertise, AI/ML understanding, and domain context. Combine adversarial thinking with model knowledge and business impact awareness. Where skills are missing, upskill teams or partner with AI security specialists.
• Integrate Red Teaming into the AI Lifecycle: Avoid one time assessments. Embed red teaming into design, pre production testing, and post deployment reviews. Retest whenever models, prompts, tools, or workflows change. This shifts AI security left and keeps pace with system evolution.
• Test Under Realistic Conditions: Mirror production as closely as possible. Use realistic data, infrastructure, user behavior, and load patterns. Findings from near real environments are more accurate and more likely to reflect real world risk.
• Balance Automation with Human Testing: Automated tools provide scale and consistency for known issues and regression testing. Human led testing adds creativity and context, uncovering novel attack paths tools may miss. The strongest programs use both.
• Document and Communicate Clearly: Translate findings into business relevant scenarios with clear impact and severity. Share results with developers, product owners, and leadership, and track remediation in a visible risk register to ensure follow through.
• Continuously Learn and Update Tests: Use each exercise to refine threat models, test cases, and development guidance. Incorporate new attack techniques as they emerge and adapt testing to new AI features and workflows.‍
• Ensure Executive Support and Governance: Red teaming only works if findings lead to action. Establish clear ownership, remediation SLAs, and escalation paths. Leadership buy-in ensures security issues are addressed, even when timelines are tight.

AI Red Teaming Tools

AI red teaming tools fall broadly into two categories: open source frameworks and commercial platforms. Leaders should evaluate them based on coverage, scalability, and operational fit.

Open Source AI Red Teaming Tools

Open source tools offer flexibility and transparency, but require internal expertise to operate and maintain.

• Promptfoo: A developer friendly framework for defining prompt based test cases and running them across models. It integrates well with CI/CD and maps findings to standards like OWASP and NIST. Best suited for early stage testing and prompt level evaluations.
• PyRIT (Python Risk Identification Tool): Developed by Microsoft’s AI Red Team, PyRIT enables scripted, multi turn adversarial testing of models and agents. It supports complex scenario simulation and deep customization, but demands strong Python and AI expertise.
• Garak: A probe based scanner for common LLM weaknesses such as jailbreaking, toxic outputs, and prompt injection. It automates conversational testing and produces structured reports, making it useful as a baseline “LLM scanner.”
• FuzzyAI: Uses fuzzing techniques to generate large volumes of prompt variations and uncover edge case failures. Effective for discovering unexpected behaviors, though findings often require manual triage.

Open source tools are ideal for teams with mature security engineering capabilities or niche requirements. The tradeoff is higher setup effort and ongoing maintenance.

Commercial AI Red Teaming Tools

Commercial solutions prioritize automation, scale, and enterprise readiness. Leading platforms combine model and agent testing, prompt injection simulations, multi agent workflow assessment, and compliance aligned reporting to ensure AI systems are secure, resilient, and auditable.

Below are the top commercial AI red teaming platforms of 2025, ranked based on automation, coverage, and integration with enterprise workflows. Each platform offers unique strengths, and organizations should evaluate based on AI system type, deployment environment, and CI/CD compatibility.

1. Levo 

Levo offers full lifecycle AI red teaming for models and agents, testing against real world attack scenarios including prompt injection, data leakage, tool misuse, and multi agent interactions. Integrates into CI/CD pipelines and provides audit ready reports.

Pros: End to end coverage of AI systems; multi agent workflow testing; runtime aware attacks; continuous testing; CI/CD and governance integration. 

Cons: Enterprise pricing; requires a team familiar with AI security concepts.

2. HackerOne AI Red Teaming 

Provides human led adversarial testing to simulate real world attacks on AI systems, producing actionable findings mapped to frameworks like OWASP and NIST. Focuses on jailbreaks, misalignment, and policy violations.

Pros: Expert human insight; prioritized, actionable reports; mapped to industry standards.

Cons: Less automated; manual testing may not scale for continuous deployment.

3. ModelRed

Automated platform for red teaming LLMs, agents, and RAG pipelines. Supports multi turn manipulation, large attack libraries, and CI/CD integration.

Pros: Automated testing; scalable multi turn attack coverage; integrates with pipelines. 

Cons: Requires configuration for custom agent workflows.

4. Giskard

Offers automated evaluation and red teaming for models, performing multi turn stress tests and mapping vulnerabilities to enterprise frameworks.

Pros: Continuous testing; executive dashboards; context dependent vulnerability detection.

Cons: Focused on models; less suited for full agent workflows.

5. Enkrypt AI Red Teaming

Covers adversarial testing across text, vision, and audio, simulating diverse attack vectors against AI agents.

Pros: Multi modal coverage; extensive threat library; enterprise reporting.

Cons: Complexity may require a dedicated security team to operate.

6. NeuralTrust AI Red Teaming

Generates domain aware test cases, automates metrics tracking, and supports repeatable red team exercises for enterprise models.

Pros: Customizable tests; business context alignment; repeatable evaluation.

Cons: Limited focus on multi agent orchestration; primarily model level testing.

7. Adversa AI GenAI Red Teaming

Provides automated continuous assessment of generative AI, covering prompt injection, data exfiltration, and other attacks. Integrates with enterprise workflows.

Pros: Continuous testing; evolving threat knowledgebase; automation friendly.

Cons: Licensing costs; may require internal expertise to interpret results.

These platforms illustrate how AI red teaming has matured into a strategic capability, combining automation, multi agent testing, and compliance focused reporting to safeguard enterprise AI systems. Organizations often adopt a mix of open source and commercial solutions to maximize coverage while maintaining scalability and audit readiness.

How Levo Helps Secure AI Agent Based Systems Using AI Red Teaming

Levo is an AI security platform designed for continuous red teaming of agent based systems. It provides end to end validation across AI agents, LLMs, tool integrations (MCPs), RAG pipelines, and APIs, ensuring vulnerabilities are caught before they reach production.

• Continuous, Runtime Aware Testing: Levo runs security tests continuously, observing the AI system in its live execution environment. It monitors agent calls, LLM responses, and tool/API interactions to simulate realistic attack scenarios. This reduces false positives and catches context dependent or non deterministic vulnerabilities. AuthN automation ensures tests mimic real world operations without manual setup.
• Full Chain Attack Simulation: Levo traces the complete agent workflow, from goal setting to MCP context retrieval, LLM prompts, API calls, and back, revealing emergent risks that arise only in multi step interactions. Its chain fuzzing approach surfaces “toxic combinations” of steps that could lead to unsafe outcomes, a coverage point often missed by traditional single request testing.
• Comprehensive Attack Classes Coverage: Levo tests a wide array of threats: prompt injections, jailbreaks, malformed inputs, data exfiltration, agent collusion, unsafe tool/plugin usage, poisoned data, and identity/permission misuse. Each scenario is automatically executed, ensuring the AI system is evaluated holistically across language, logic, and identity layers.
• Privilege Simulation and Identity Testing: Levo models non-human identities, tokens, and API keys to test privilege boundaries. It simulates scenarios like token swapping, escalated scopes, and temporary admin access to ensure agents cannot step outside their authorized privileges, closing gaps that traditional IAM testing might miss.
• Language Layer Probing: Levo actively tests the AI’s prompt and retrieval layers, injecting variations to trigger semantic failures. This exposes vulnerabilities like instruction overrides, jailbreaks, or sensitive data leaks that static analysis cannot detect, acknowledging the AI model itself as an attack surface.
• Validation to Reduce Noise: Levo validates exploitability in context, showing concrete evidence when a vulnerability is triggered. By correlating behavior across the workflow, it filters out hypothetical warnings and prioritizes findings, enabling security teams to focus on actionable, reproducible issues.
• Developer and DevOps Friendly: Levo integrates seamlessly into CI/CD pipelines, automates scenario and auth setup, and produces actionable logs. Developers can see exactly which step or condition triggered an unsafe action, allowing fast and confident remediation. Security and compliance teams benefit from prioritized findings and audit ready evidence without slowing development

Benefits Across Teams:

• Developers: Faster, safer feature deployment with early issue detection.
• Security Teams: Focused, validated risks without false positives.
• Compliance: Immutable logs and reports for regulator ready proof of controls.
• Executives: Confidence in AI deployment speed, safety, and customer trust.

Example Use Case: Deploying AI customer service agents with Levo enables continuous testing for prompt injections, data exposure, and unsafe tool usage. Vulnerabilities are flagged with full evidence, fixes are applied, and subsequent tests confirm resolution. The result: AI agents go live with significantly reduced risk.

Levo operationalizes AI red teaming by combining automation, realistic attack simulations, and intelligent validation. It transforms red teaming from a noisy checkbox into a business enabler, detecting real risks, filtering false positives, and providing evidence to deploy AI faster, safer, and with lasting trust.

The Way Ahead: Implementing Total AI Security Beyond Red Teaming

AI red teaming is a critical first step, but it’s only part of a comprehensive AI security strategy. To achieve “total AI security,” organizations need to extend protections across the full AI lifecycle, from design to retirement.

• Discovery and Inventory: Secure what you know exists. Maintain an up to date inventory of all AI systems, agents, models, and integrations. Shadow AI applications can emerge unnoticed, so leverage discovery tools, network monitoring, and governance processes. Platforms like Levo can automatically identify AI endpoints and data flows from live traffic, giving full visibility into where security measures are required.
• Policy Definition and Training: Develop clear AI policies covering permitted data access, actions, and prohibited behaviors. Train AI systems (via prompt design or fine tuning) and employees on these policies. Policies guide red teaming and ensure monitoring enforcement aligns with organizational expectations.
• Continuous Monitoring in Production: Post deployment monitoring is essential. Track anomalies in agent actions, model drift, or suspicious prompt activity. Runtime monitoring tools, such as Levo’s eBPF based platform, capture live AI interactions and flag unsafe behavior in real time. Think of this as an AI specific IDS/IPS layer, catching threats that pre deployment tests might miss.
• Incident Response and Playbooks: Treat AI incidents like cybersecurity events. Define alerting, containment, and stakeholder communication processes. Include forensic steps to preserve logs and data for analysis. A formal AI incident response playbook ensures swift, coordinated action and minimizes damage.
• Governance and Change Management: Embed AI risk oversight into existing governance frameworks. Establish committees to review red team findings, approve high risk deployments, and track mitigation progress. Manage changes carefully: new models, tools, or agents should undergo security review and retesting. Gatekeeping ensures consistent protection across all AI projects.
• Lifecycle Perspective “Secure AI DevOps”: Apply a lifecycle view to AI security. Integrate secure model training, periodic re-evaluation, continuous red teaming, and end of life data handling. This ensures security isn’t just front loaded but maintained continuously, adapting to evolving AI capabilities and threat landscapes.
• Integration with Broader Security Infrastructure: Treat AI systems as part of the enterprise attack surface. Feed logs and red team results into SIEMs, train SOC analysts on AI alerts, and include AI scenarios in red team/blue team exercises. This embeds AI security within the organization’s overall cybersecurity posture.

Organizations that implement full spectrum AI security, starting with design policies, adversarial testing, and extending to runtime monitoring and governance, will confidently scale AI initiatives. Red teaming provides immediate insights and hardens systems upfront, but total AI security ensures continuous prevention, detection, and response.

Platforms like Levo unify pre deployment testing and runtime protection, creating an integrated security framework.

By treating AI security as a continuous lifecycle and leveraging robust practices like red teaming, companies can mitigate risks, accelerate safe AI adoption, and meet emerging regulatory expectations. Red teaming is the foundational pillar; layered controls, monitoring, and governance turn it into a resilient, enterprise grade AI security strategy. With this approach, AI becomes a trusted business enabler, not a source of uncontrolled risk.

Levo delivers full spectrum Runtime AI detection and protection with full governance and visibility for modern organisations. Book your Demo today to implement AI security seamlessly.