Continuous AI red teaming for secure & rapid AI productionization
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AI built to drive growth becomes a liability without security testing
Legacy testing assumes deterministic paths the same input produces the same output. That’s why scanners can map, replay, and fuzz exhaustively. AI agents, by contrast, generate plans dynamically one goal can produce endless tool/API sequences. Traditional tests collapse here because they can’t simulate or validate emergent, runtime only behaviors.
Legacy tools test for code flaws, parameter tampering, and structured payload errors. They were never built to probe semantic vulnerabilities prompts that jailbreak a model, socially engineer an agent, or manipulate embeddings. As a result, prompt injection and language driven exploits slip entirely past static fuzzers and scanners.
Conventional IAM tests were designed for human logins and static service accounts. They can validate role maps, tokens, and auth flows, but they can’t test how delegated AI identities behave in practice. Agents and MCPs acquire just in time, scoped tokens, swapping them mid session. Legacy testing misses privilege escalation and identity misuse because it doesn’t model these non-human actors.
Traditional testing validates discrete request response pairs. AI applications, however, form opaque chains to an agent invoking an MCP, which queries an LLM, which in turn calls APIs or other agents. Vulnerabilities emerge in the chain, not the single call. Edge scanners and static tests fail because they don’t see, let alone fuzz, these recursive multi hop interactions.
Deploy AI into production with confidence
because security testing detects only real risks
Agent security & control
Levo probes agent behavior across tool invocation, input handling, collusion, and plugin vulnerabilities to ensure agents can’t overstep, collude, or mishandle sensitive data. This keeps autonomous workflows bounded, regulator approved, and safe to scale.
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Model robustness & integrity
From prompt injection and adversarial datasets to hallucinations, extraction, and data poisoning, Levo validates that LLMs behave as intended, resist subversion, and don’t leak IP or customer data, preserving trust and compliance at scale.
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MCP governance assurance
Levo verifies that MCP servers enforce whitelisted functions, role based policies, and secure connectors. By tightening this trust boundary, enterprises gain confidence that multi-agent orchestration executes only what’s authorized.
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Levo unites every team to defend against the biggest risk obsolescence from slow AI adoption.
Ship AI features faster as red teaming validates agents, models, and MCPs automatically, converting velocity into safe, revenue driving deployments.
Stop chasing noise; focus only on validated risks. Levo surfaces exploitable issues so security posture improves with every production rollout.
Achieve effortless audit success as every red team test produces immutable, regulator ready evidence governing AI from day one instead of scrambling later.
Faster builds, fewer breaches, smoother audits.
Frequently Asked Questions
Got questions? Go through the FAQs or get in touch with our team!
What is Levo’s AI Red Teaming?
Continuous, runtime aware security testing for AI applications that spans agents, LLMs, MCP servers, RAG, and APIs. It validates only exploitable risks, filters noise, and delivers audit ready evidence so you can ship AI faster and safer.
Why do AI apps need continuous testing, not point in time scans?
AI behavior changes with prompts, data, tools, and models. Point scans miss non deterministic plans, chained calls, and language layer exploits, which are where real failures emerge.
How is this different from legacy scanners and fuzzers?
Legacy tools assume deterministic paths and code level payloads. Levo models semantic attacks, agent decisions, and multi hop chains, then verifies impact in real runtime contexts.
What attack classes does Levo simulate?
Prompt injection and jailbreaks, adversarial inputs, data leakage and extraction, agent collusion and overreach, unsafe tool invocation and plugin misuse, poisoning risks, and identity misuse across delegated tokens.
Can Levo handle non deterministic agent workflows?
Yes. Levo exercises goal driven plans that can branch into many tool and API sequences, then validates outcomes to separate real risk from harmless variance.
How does Levo test the language layer?
It probes prompts, instructions, and retrieved context to trigger semantic failures, including jailbreaks, instruction overrides, and embedding manipulation that static tests cannot see.
How are non-human identities validated?
Levo tests delegated and just in time tokens used by agents and MCPs, checking for privilege escalation, scope drift, and identity misuse that traditional IAM tests do not model.
Does Levo see issues that arise only in chains, not single calls?
Yes. It follows agent to MCP to LLM to API to agent loops, surfaces emergent risk in the chain, and validates the full exploit path, not just one request.
What is Runtime Aware Testing and AuthN Automation?
Tests are rooted in runtime visibility and handle authentication automatically, so scenarios are realistic, reproducible, and low noise, with fewer false positives and faster fixes.
How does Levo reduce false positives?
By proving exploitability in context, correlating behavior, identity, and data movement across the chain, and reporting only issues with demonstrated impact.
What does Agent Security and Control include?
Probing tool invocation, input handling, schema safety, and collusion behavior, ensuring agents cannot overstep intent or mishandle sensitive data.
What does Model Robustness and Integrity cover?
Resistance to prompt injection, adversarial datasets, hallucination risks, extraction, and poisoning, with checks that models behave as intended and do not leak IP or customer data.
How is MCP Governance assured?
Levo verifies whitelisted functions, role policies, and connector safety at the MCP boundary, tightening control over multi-agent orchestration.
Where does this run in the lifecycle?
Continuously in pre-production and against production realistic traffic patterns, with results designed to accelerate developer velocity and operational confidence.
What evidence do we get for audits?
Immutable, regulator ready findings that record who acted, what data moved, where it went, and how a risk was proven, so compliance moves from scramble to routine.
How does this help Developers, Security, and Compliance?
Developers ship faster with validated guardrails, Security focuses on exploitable issues instead of noise, Compliance gets automatic, trustworthy evidence from day one.
Will this create tool or team overhead?
Levo automates scenario setup, auth handling, and validation, avoiding bloated headcount while increasing coverage across agents, LLMs, MCPs, RAG, and APIs.
How does Levo prioritize what to fix first?
Findings include exploit paths and business context, so teams can address the highest impact risks that block safe releases or violate policy.
Can it plug into CI/CD and existing workflows?
Yes. Results are structured for gating, triage, and remediation workflows, aligning testing outcomes with developer speed and security objectives.
What outcomes should we expect?
Faster productionization, fewer breaches, shorter audit cycles, and lower incident response and compliance costs, without trading growth for control.
What is the UVP in one line?
Continuous AI red teaming for secure and rapid AI productionization, dynamic testing across agents, LLMs, MCPs, RAG, and APIs.
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