AI Security Company for AI & LLM Systems

An AI security company protects artificial intelligence and LLM systems from adversarial attacks, prompt injection, data poisoning, and model theft. Berghem is a Brazilian AI security company serving LATAM, delivering AI/LLM penetration testing, Red Team for AI, and governance aligned with ISO 42001, the EU AI Act, and NIST AI RMF.

Agentic systems widen this attack surface — to secure autonomous agents with tool and memory access, see AI agent security.

LLM security is the discipline of protecting large language models and the stack that puts them into production — system prompts, guardrails, retrieval pipelines (RAG), tool calls, memory, plugins and the outputs that feed downstream systems. Unlike traditional applications, an LLM does not separate data from instructions: any text that enters the context can be interpreted as a command. This blurs the trust boundary and creates classes of attack that do not exist in conventional software.

In practice, securing an LLM means treating every point where untrusted text meets the model as an attack surface. A document retrieved by RAG, the conversation history, the content of a page the agent browses, the return value of a tool — all of them can carry malicious instructions. That is why Berghem assesses the system end to end, not just the model in isolation: the risk almost always lies in how the model is integrated, orchestrated and exposed.

LLM security rests on three complementary fronts. The offensive front — LLM pentesting and adversarial Red Team — exercises the system with prompt injection, jailbreaks, system prompt extraction, insecure tool invocation and adversarial inputs, producing reproducible proof of exploitation. The defensive front hardens guardrails, validates and sanitizes outputs, applies least privilege to tools and caps resource consumption. The governance front ensures that decisions, training data and suppliers are traceable and auditable.

Each dimension calls for its own controls. Input handling must assume that all text in the context is hostile until proven otherwise. Output handling must validate, escape and never blindly execute what the model returns. The tool layer must grant the smallest possible set of permissions and require confirmation for sensitive actions. Training and fine-tuning data need verifiable provenance to resist poisoning. And consumption — tokens, calls and cost — must have limits that prevent abuse and denial of service.

An LLM's threat model differs fundamentally from that of a traditional API. In an API, the boundary between code and data is rigid and known; in an LLM, that boundary is probabilistic and depends on the content itself. An attacker needs neither a memory flaw nor an SQL injection — it is enough to craft text persuasive enough that the model prioritizes the malicious instruction over the system policy. Indirect prompt injection makes it worse: the hostile payload can sit in an email, a web page, a PDF or a database record that the model merely reads. That is why an LLM's attack surface includes everything that can end up in its context, not just the interface exposed to the end user.

Berghem's approach to LLM security starts with a survey of the architecture and data flows, followed by AI-specific threat modeling using the OWASP LLM Top 10, MITRE ATLAS and the Promptware Kill Chain. From there we run targeted offensive tests — direct and indirect prompt injection, jailbreaks, system prompt extraction, tool abuse and RAG poisoning — and document each exploitation with reproducible evidence. We close the loop with remediation recommendations prioritized by risk and, when the client wants it, continuous monitoring that detects regressions and new vectors as the system evolves. The goal is not just to find flaws, but to leave the system measurably harder to compromise.

AI agents go beyond replying with text: they plan, decide and act — invoking tools, browsing the web, executing code and keeping memory across steps. Each of these capabilities widens the attack surface. An agent compromised by indirect prompt injection can exfiltrate data, chain dangerous tool calls or persist malicious instructions in its memory, turning a single hostile input into a chain of autonomous actions. Securing agents requires least privilege per tool, human confirmation for sensitive actions, memory isolation and an auditable record of every decision. Go deeper into AI agent security to understand how we assess and harden agentic systems in production.

Every security testing provider promises to find vulnerabilities. Few can guarantee that each finding in the report is real, exploitable and correctly prioritized — that is the difference between a report that drives action and a report that drives rework.

A single false positive reaching the client is not operational noise: it is erosion of trust. The client loses hours investigating something that does not exist, questions every other finding by association, and the reputational cost outweighs the value of the entire engagement. Our methodology starts from a non-negotiable premise — no finding reaches the client without mechanical evidence that makes it defensible.

For traditional applications and APIs, the same rigor guides our pentest — Berghem's manual penetration testing across Silver, Gold and Diamond tiers.