AI Security
Agent Security
LLM-powered applications introduce an entirely new attack surface: direct and indirect prompt injections, jailbreaks engineered to bypass safety guardrails, RAG vector database poisoning, system prompt extraction, and remote code execution or SSRF via unconstrained tool calling.
Description
LLM-powered applications introduce an entirely new attack surface: direct and indirect prompt injections, jailbreaks engineered to bypass safety guardrails, RAG vector database poisoning, system prompt extraction, and remote code execution or SSRF via unconstrained tool calling.
We perform penetration testing, red-teaming, and specialized security hardening for LLM applications. We evaluate and fortify systems against the OWASP Top 10 for Large Language Model Applications, deploying prompt firewalls, input/output validators, and sandboxed execution environments for secure code and tool execution.
As a specialized capability, Agent Security provides a focused approach to the technical and strategic requirements of the AI Security domain. We design custom operational protocols, implement rigorous validation, and give the organization full control over this critical factor to secure a sustainable competitive advantage across the language model and generative AI ecosystem.
Impact Thesis & Return on Investment
System resilience against malicious manipulation attacks, protection of proprietary prompts and data intellectual property, and prevention of unauthorized internal access via AI interfaces. In today's corporate ecosystem, securing presence and accuracy across foundation models is not merely a competitive advantage, but a critical defensive barrier against the disintermediation of traditional digital traffic.
Methodology
Manual and automated red-teaming against models and autonomous agents; simulation of indirect prompt injection attacks via documents; data leakage assessments and strict sandboxing; deployment of real-time AI firewalls.
Technology Stack
- Technology Ecosystem: OWASP LLM Top 10, Garak, PyRIT (Microsoft), NeMo Guardrails, Rebuff, Docker Sandboxing, Langfuse Security Alerts.
- Semantic Standards: W3C Semantic Web Standards, Schema.org @graph, RDF/OWL ontologies, OpenAPI 3.1.
- Validation Protocols: Continuous evaluation with LLM-as-a-Judge, semantic guardrails, and regression tests in continuous integration pipelines.
Deliverables
- Penetration testing and red-teaming report specialized for AI applications.
- Vulnerability matrix with proof of concept and exploitation vectors.
- Implementation of defense-in-depth architecture and security guardrails.
- AI security evaluation certificate for enterprise clients and audits.
Success KPIs
- 0% critical prompt injection or jailbreak vulnerabilities in production.
- Successful real-time blocking of malicious manipulation attempts (>99.9%).
- Strict isolation of tool calls and code execution environments.
- Full compliance with OWASP LLM Top 10 security recommendations.
Who is it for?
Who it is for
Chief Information Security Officers (CISOs), fintech enterprises, B2B platforms processing customer data, and SaaS providers.
Who it is NOT for
Informational websites without integrated AI applications, agents, or conversational interfaces.
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