Adversarial Security Testing for LLM and ML Applications

What AI Security Testing Actually Delivers

A useful engagement produces the same concrete outputs as any well-run security assessment, adapted for the specific character of AI attack surface:

• Reproducible exploit paths, not model-behavior anecdotes. Every finding is documented with the specific input sequence that produced it, the conditions under which the model responded that way, and the downstream consequence.
• Impact framed by the deployment, not the model. The interesting question is rarely whether a base model can be jailbroken in principle. It is what an attacker can actually reach through the deployment as configured: which functions can be called, which data can be exposed, which downstream systems can be touched.
• Remediation guidance that survives the next model update. Defenses anchored on prompt-engineering alone break when the model changes. The report prioritizes architectural mitigations (input isolation, output validation, authorization layered outside the model) that survive model swaps.
• Documentation for regulator, carrier and counsel. The report is written to support AI governance obligations, insurance renewal questions specific to AI deployments, and any incident record the deployment may later have to defend.
• Closure re-test after remediation. The same attack paths that surfaced findings are re-run against the fixed system, and the closure record is a documented artifact.

The Attack Classes That Actually Matter

Testing scope is anchored on OWASP’s Top 10 for LLM Applications and MITRE ATLAS, extended with the specific attack patterns that have surfaced in real deployments. The engagement is tailored to which classes are actually reachable given the deployment’s architecture.

Prompt injection, direct and indirect

Direct: a user attempting to override the model’s instructions with their own inputs. Indirect: attacker-controlled content reaching the model through a legitimate channel (documents ingested for summarization, retrieved context, tool outputs, web content the model is asked to summarize). Indirect injection is the more consequential class in practice, because it converts any content-ingestion path into an attacker-controlled channel into the model’s instruction context.

Jailbreaks against safety training

Bypasses that induce the model to produce output the safety training was intended to prevent, via role-play framings, hypothetical scaffolding, encoding tricks, multilingual pivots, and iterative refinement. Testing focuses on whether these bypasses produce consequential outcomes in the deployment, not on cataloguing bypasses in principle.

Function-calling and tool-use abuse

Where the model has access to functions or tools that touch real systems, whether the model can be induced to call functions the caller should not have access to, or to call authorized functions with parameters that escape the caller’s scope. This is the highest-consequence class in most modern deployments and the class most likely to be underspecified in the initial threat model.

Retrieval-augmented generation abuse

Where the model retrieves from a document store, whether an attacker can inject content into the retrieval corpus that will subsequently influence the model’s behavior for other users, and whether retrieved content can carry injection payloads that reach the instruction context.

Model extraction and inversion

Where the model or its training data has value that should not be extracted, whether an attacker with query access can reconstruct the underlying model behavior, membership-infer training-data records, or extract sensitive fine-tuning content through targeted prompting.

Data poisoning during fine-tuning and retrieval

Where fine-tuning or retrieval data comes from external sources, whether an attacker can influence that data to shape future model behavior, and whether the ingestion pipeline validates content before it reaches the training or retrieval path.

Denial-of-wallet and cost abuse

Whether the deployment can be induced to consume disproportionate model resources on attacker-supplied inputs, converting an AI feature into a cost-amplification attack against the operator.

How We Test

Scope is defined in writing before testing starts: the deployment’s architecture, the model versions and providers in play, the data sources feeding retrieval and fine-tuning, the tools and functions the model can invoke, the safety and moderation layers already in place, and the specific concerns the client wants tested. Testing runs against the deployment as configured, not against the base model in isolation, because the base model’s behavior in a vendor sandbox is rarely the interesting question.

Adversarial input generation combines a curated library of known-effective attack templates with campaign-specific variants developed against the client’s deployment. Automated fuzzing surfaces the classes of finding it is good at; manual adversarial testing surfaces the ones that require adaptive reasoning about the deployment. Where the client permits access to a staging environment, testing runs against staging first with the same data profile as production, then confirms critical findings against production under controlled conditions.

Every reproducible finding is documented with the specific input, the response, the downstream consequence, and the conditions required. Findings are ranked by exploitability in the deployment as configured and by the blast radius of the downstream systems the finding reaches.

Machine-Learning Model Testing Beyond LLMs

Not every AI system is an LLM. Classical machine-learning models in fraud detection, risk scoring, medical decision support, computer vision and predictive maintenance carry their own adversarial attack surface: adversarial examples that flip classifications, evasion attacks against fraud and abuse detection, model-inversion attacks that recover training data, and membership-inference attacks against models trained on sensitive datasets. Testing scope is adapted to the model class in use, but the reporting discipline is the same: reproducible, deployment-anchored, and structured for the audience that has to act on it.

Where Our Miami AI Testing Practice Runs Deepest

Fintech, crypto and digital-asset AI features

Customer-facing assistants with function-calling access to account operations. Automated trading, market-surveillance and fraud-detection models. Compliance-focused AI systems where the deployment’s conduct is subject to examination. The Miami digital-asset cluster is one of the densest concentrations of consequential AI deployment work in the country, and testing scope is anchored on the specific attack surface these deployments actually carry.

Hospitality and cruise-line AI

Guest-facing chatbots, booking-assistant deployments and revenue-management AI systems. Testing focused on the specific abuse patterns these deployments face: fare-manipulation attempts, room-inventory abuse, and prompt-injection through user-supplied content in reservation flows.

Healthcare and clinical AI

Clinical decision-support systems, patient-facing conversational AI and administrative AI systems in regional hospital environments. Testing framed against HIPAA obligations and the specific evidentiary standards clinical AI systems face post-incident.

Aviation and connected-infrastructure AI

AI and machine-learning components in aviation software and connected-infrastructure systems, tested against the specific adversarial-robustness expectations these systems face under their airworthiness and safety frameworks.

Cross-border and multilingual deployments

Deployments serving Latin American markets, where adversarial testing needs to run in Spanish and Portuguese as well as English, because prompt-injection and jailbreak surface differs across language boundaries.

Standards and Standing

Methodology anchored on OWASP Top 10 for LLM Applications, MITRE ATLAS (Adversarial Threat Landscape for AI Systems), NIST AI Risk Management Framework, ISO/IEC 42001 AI management systems standard, and the emerging body of adversarial-robustness literature. Testers hold OSCP, OSWE, GXPN and CISSP among other credentials, and have specific practical experience with the current generation of LLM and ML attack tooling. Reports are structured for authentication under Fla. Stat. § 90.901 and Federal Rules of Evidence 902(13) and 902(14), and are written to support AI governance obligations under both federal and state frameworks.