Watch our technical briefing to see exactly how emerging AI vulnerabilities map to real-world CVEs, and get the defensive baselines you need to secure your deployments.
Bridge the Gap Between Rapid AI Adoption and Enterprise-Grade Security
Move beyond simple prompt injection. Elite manual testing and OWASP ASI alignment for your autonomous agents and LLM ecosystems.
Your AI is Moving Faster Than Your Security Program
As organizations rush to integrate LLMs and autonomous agents, they create dynamic, unpredictable environments that traditional security scanners are not equipped to handle.
The "Noise" Problem
Automated tools produce excessive false positives and fail to uncover the sophisticated logic flaws inherent in agentic workflows.
Probabilistic Risk
Traditional pentesting treats threats as deterministic. AI threats are probabilistic, requiring a manual-first approach to translate into business risk.
Adversarial Focus
We don't just scan your AI, we pressure-test it. We think like attackers who are currently weaponizing AI to exploit your environment.
Why Manual Testing is the Gold Standard for AI
Adversarial DNA
Built on 18+ years of elite red teaming, our expertise is rooted in the same mindset as your most dangerous adversaries.
Beyond the Chatbot
We assess the entire ecosystem (non-human identities, API integrations, and multi-step autonomous reasoning), not just prompt injection.
Tailored Methodologies
Our testing methodologies are built on the OWASP Agentic AI Top 10 and the MITRE ATLAS framework, ensuring your defenses are pressure-tested against the latest industry-standard vulnerabilities and real-world adversarial tactics.
Security Tailored to Your AI Maturity Level
Our 4-Stage Model ensures you receive the right depth of testing for your specific adoption phase.
Stage 1:
AI Attack Surface Assessment
The Foundational Step.
Map your AI exposure before attackers do. Visibility into agents, server-side LLM integrations, and non-human identities.
Stage 2:
AI Application Penetration Test
Target Validation.
Active testing of specific features, like customer-facing chatbots or internal knowledge retrieval (RAG) systems.
Stage 3:
AI Agent & Automation Security Test
The Next Frontier.
Testing autonomous logic. Can agents be manipulated into bypassing guardrails or escalating privileges?
Stage 4:
AI Red Team Simulation
The Ultimate Stress Test.
Objective-based simulation using AI-specific TTPs to prove real-world resilience.
Secure Your AI Ecosystem Before Attackers Do
Book a brief scoping call with our adversarial engineers to identify the right testing stage for your organization.
The Lares Blueprint for AI Resilience
A framework for building continuous AI resilience across your organization.
You cannot secure what you cannot see. Agentic ecosystems dynamically load external tools and personas at runtime, making static inventories obsolete.
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Map Non-Human Identities (NHIs): Track every token, certificate, and API key your agents use to interact with internal databases and external services.
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Audit the Supply Chain: Catalog all Model Context Protocol (MCP) servers, third-party plugins, and external agent integrations.
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Identify Shadow AI: Hunt for unsanctioned agentic workflows operating outside of governed IT and security guardrails.
Not all AI risks apply equally. We use the OWASP Agentic Security Initiative (ASI) Top 10 and MITRE ATLAS framework to map emerging threats directly to your unique business logic and architecture.
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Baseline the Top 10: Walk through ASI01 through ASI10 to understand how risks like Agent Goal Hijack and Memory Poisoning apply to your specific use cases.
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Assess the Action Chain: Evaluate vulnerabilities across the entire agent lifecycle, from user input and integration processing to final tool execution.
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Assume Compromise: Design threat models that account for the failure or exploitation of any single agent or component.
Chatbots required output filtering. Autonomous agents require strict execution boundaries and intent validation.
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Implement Intent Gates: Utilize policy enforcement middleware that explicitly separates an agent's planning phase from its execution phase.
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Deploy JIT Credentials: Abandon static API keys. Equip agents with Just-In-Time, short-lived credentials that expire immediately after a task is completed.
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Restrict Tool Access: Never grant write, delete, or external transfer capabilities to agents performing read-only or summarization functions.
Because agents delegate autonomously, a single fault can compound into system-wide harm. Your architecture must be designed to halt cascading failures before they outpace human incident response.
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Establish Blast-Radius Caps: Set hard limits on the number of API calls, financial transactions, or data queries an agent can execute within a specific timeframe.
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Deploy Circuit Breakers: Automatically isolate and quarantine failing or misbehaving agents from the rest of the network.
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Require Human-in-the-Loop (HITL): Mandate cryptographic authentication and explicit human approval for any high-impact, destructive, or financial actions.
Traditional rule-based monitoring cannot detect goal-level deviations in autonomous agents. You need AI-native observability to detect Rogue Agents and workflow hijackings.
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Deploy Watchdog Agents: Use specialized, isolated agents whose sole purpose is to monitor peer agent outputs, validate intent, and flag behavioral anomalies.
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Enable Distributed Tracing: Maintain immutable, cryptographically signed audit logs of all agent actions, tool calls, and inter-agent communication.
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Run AI-Specific Purple Teaming: Continuously stress-test your environment using adversarial emulation and AI-specific incident response playbooks.
Looking for something else?
Empowering Organizations to Maximize Their Security Potential.
Lares is a security consulting firm that helps companies secure electronic, physical, intellectual, and financial assets through a unique blend of assessment, testing, and coaching since 2008.
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