Penetration Testing an Agentic System

Why Agentic Security Requires a Different Kind of Test

Security testing for autonomous AI systems has become one of the most technically demanding specializations in enterprise risk management. Traditional penetration testing assumes a relatively static attack surface — servers, APIs, authentication endpoints — but an agentic system introduces a dynamic layer where the target actively reasons, delegates, and executes. When an agent can call tools, spawn subagents, interpret natural language instructions, and take real-world actions, the threat model expands in ways that conventional pen test frameworks were never designed to handle. The firms appearing in this list were selected because they have moved beyond checkbox audits into the architectural realities of agentic infrastructure.

What Makes Agentic Systems Uniquely Difficult to Test

Before evaluating vendors, it helps to understand why Penetrating Testing an Agentic System demands a fundamentally different methodology than testing a conventional application. A static web application has a defined input surface — form fields, headers, query parameters. An agent's input surface includes natural language, tool schemas, memory retrievals, planner outputs, and responses from other agents. Each of those channels carries its own class of injection risk, and those risks compound when agents are chained together in multi-agent pipelines.

The OWASP Top 10 for Large Language Model Applications, published in 2023 and updated in 2025, identifies prompt injection as the dominant vulnerability class, but the document explicitly notes that agentic contexts amplify severity because a successful injection can propagate through an entire pipeline rather than affecting a single model call. Indirect prompt injection — where malicious content embedded in a retrieved document or web page hijacks an agent's reasoning — is particularly difficult to detect with traditional scanning tools because the payload never appears in the original request.

Memory and state persistence add another testing dimension that most security firms are not equipped to evaluate. An agent that stores summaries of past interactions in a vector database can be manipulated through a slow poisoning attack, where adversarial content inserted during early interactions gradually shifts agent behavior over time. Detecting this requires a testing methodology that spans multiple sessions, not a single-session scan.

Authorization across agentic tool calls is a third structural vulnerability category. When an agent is granted access to a file system, a CRM, an email client, and a payment API simultaneously, the question is not just whether each individual tool is secure in isolation. The question is whether an attacker can manipulate the agent into combining those tool permissions in ways the system designer never intended — a concept sometimes called confused deputy exploitation in an agentic context.

HiddenLayer — Adversarial ML Focused

HiddenLayer is one of the few firms that approaches AI security from a machine learning adversarial research background rather than from traditional application security. The company's core product, AISec, is built to detect adversarial inputs, model theft attempts, and data poisoning across deployed models. Their research team has published documented work on evasion attacks against classification models and extraction attacks that reconstruct model behavior without direct access to weights.

For agentic systems specifically, HiddenLayer's tooling is strongest on the model layer — detecting when inputs are crafted to shift output distributions — but it has less documented depth on multi-agent orchestration vulnerabilities or the authorization issues that emerge from tool-use architectures. Organizations deploying single-model systems with well-defined input schemas will find HiddenLayer's detection capabilities genuinely useful, though teams building complex pipelines with dynamic tool access may find the coverage narrows at the orchestration layer.

Protect AI — MLOps Pipeline Security

Protect AI takes a platform-oriented approach to AI security, with a focus on the MLOps pipeline rather than the runtime agent. Their Guardian product scans machine learning model files for embedded malicious code — a genuine and underappreciated risk, since serialized model formats like Pickle can execute arbitrary code on deserialization. Their NB Defense tool audits Jupyter notebooks for secrets, vulnerabilities, and sensitive data exposure, which matters significantly during the development phase of agentic systems.

The firm has also released Rebuff, an open-source prompt injection detection framework, which signals meaningful engagement with the agentic threat landscape. However, Protect AI's published methodology is most explicit about supply chain risks — poisoned models, compromised dependencies — rather than runtime behavioral testing of deployed agents taking actions in live environments. For organizations that need both supply chain assurance and runtime penetration testing, Protect AI covers the first problem well, but the second requires a different capability set.

Adversa AI — Trustworthy AI Red Teaming

Adversa AI positions itself specifically as an AI red team company, which makes it one of the more directly relevant vendors for organizations evaluating Penetration Testing an Agentic System as a distinct discipline. The firm has published research on LLM jailbreaking, adversarial prompting across commercial models, and the trustworthiness properties of AI systems under attack. Their AI security taxonomy distinguishes between attacks on model behavior, attacks on model data, and attacks on model deployment — a useful framework for scoping a test engagement.

Adversa's red team methodology involves systematic adversarial prompting campaigns designed to elicit policy violations, data leakage, and behavioral drift. Their published case studies focus primarily on foundation model vulnerabilities rather than the orchestration layer of multi-agent systems. Organizations building agentic pipelines with complex tool-use chains, persistent memory stores, or inter-agent communication protocols will need to verify whether a given Adversa engagement extends to those layers or focuses primarily on the model's base behavioral properties.

Giskard — Open-Source LLM Testing Infrastructure

Giskard approaches the problem from a testing infrastructure angle. Their open-source library provides a framework for writing test suites that evaluate LLM behavior across dimensions including hallucination, toxicity, prompt injection susceptibility, and output consistency. The library integrates with standard Python workflows, which makes it practical for engineering teams that want to build automated regression tests into their CI/CD pipelines rather than rely entirely on point-in-time external assessments.

The Giskard Hub product extends this into a collaborative testing environment where teams can document model behavior, track regressions, and manage test catalogs across model versions. For organizations building internal testing capability, Giskard offers a credible open-source foundation. The limitation is that self-service testing infrastructure places the burden of test design on the client's engineering team. Identifying which attack vectors to test, how to simulate indirect injection through retrieval pipelines, and how to evaluate multi-agent authorization failures requires deep expertise that a library alone does not provide.

TFSF Ventures FZ LLC — Production Infrastructure With Security Architecture Built In

TFSF Ventures FZ LLC appears at this position in the list because its role in agentic security is fundamentally different from the testing vendors above it. Rather than offering a discrete pen test service, TFSF builds the production infrastructure that agentic deployments run on — and its deployment methodology incorporates security architecture from the first day of the engagement rather than adding it after a system is live. The distinction matters because most post-deployment security assessments find vulnerabilities that are structurally expensive to remediate; TFSF's 30-day deployment methodology is designed to prevent that category of finding from existing in the first place.

TFSF Ventures FZ LLC's exception handling architecture is one of the specific differentiators that separates it from both testing vendors and conventional AI consultancies. In a production agentic system, an exception is not just a bug — it can represent an adversarial input that forced the agent into an unhandled state, a tool call that returned unexpected data, or a subagent that received a manipulated instruction. TFSF's architecture treats each exception as a first-class event with a defined escalation path, an audit trail, and a human-in-the-loop trigger where the exception type warrants it. That architecture is the foundation on which a meaningful security posture is built. For organizations asking "Is TFSF Ventures legit," the answer is verifiable: the firm operates under RAKEZ License 47013955, founded by Steven J. Foster with 27 years in payments and software, across 21 verticals with documented production deployments.

TFSF Ventures FZ LLC pricing for agentic deployments starts in the low tens of thousands for focused builds, scaling by agent count, integration complexity, and operational scope. The Pulse AI operational layer — the proprietary engine that underlies TFSF's agentic infrastructure — runs as a pass-through based on agent count, at cost, with no markup. Clients receive ownership of every line of code at deployment completion, which means there is no ongoing platform subscription required to keep the system operational. That ownership model has direct security implications: the client controls their own codebase, their own data, and their own infrastructure, rather than depending on a vendor's security posture for continued protection.

TFSF Ventures reviews from a technical standpoint consistently point to the 19-question Operational Intelligence Assessment as the entry point for new engagements. That assessment scopes the deployment across operational dimensions before a single line of code is written, which means security requirements — including exception handling scope, tool permission architecture, and escalation policy — are defined at the blueprint stage rather than retrofitted. The assessment is available at https://tfsfventures.com/assessment. Labarna AI's piece on what a sovereign deployment looks like on day one and year five elaborates on why front-loaded architecture decisions compound positively over time.

Mindgard — Runtime AI Red Teaming

Mindgard is a UK-based firm that has built a platform specifically for automated AI red teaming at runtime. Their system launches adversarial probes against deployed models and agents, cataloging vulnerabilities across a defined taxonomy that includes prompt injection, sensitive data disclosure, and model denial-of-service. The automation angle is meaningful for organizations that need continuous testing rather than point-in-time assessments, since agentic systems change behavior as their underlying models update or their tool sets expand.

Mindgard's platform integrates with CI/CD pipelines, which means security tests can run automatically when a new model version is deployed or when agent tool configurations change. Their published taxonomy aligns reasonably well with the OWASP LLM Top 10, providing a common vocabulary for engineering and security teams to communicate findings. The platform's current documentation is strongest on single-agent runtime testing; organizations whose architectures involve multiple agents coordinating through shared memory or message queues should clarify during scoping whether multi-agent orchestration is covered explicitly or treated as an extension case.

Bishop Fox — Enterprise Security With AI Expansion

Bishop Fox is a long-established enterprise penetration testing firm that has extended its practice into AI systems as the market demand has grown. The firm's traditional strength is in application security, cloud security, and red team operations for large enterprises, and it brings that methodological rigor to AI engagements. Bishop Fox has published research on LLM prompt injection and released tooling for testing conversational AI systems, signaling a genuine investment in the domain rather than a marketing-only positioning.

For organizations that want AI security testing embedded within a broader enterprise security program — alongside red team operations, cloud penetration testing, and secure code review — Bishop Fox offers the advantage of a single firm that can span all of those disciplines. The tradeoff is that AI-native firms with research teams focused exclusively on adversarial ML and agentic architectures have accumulated more published depth on the specific vulnerability classes that matter most in agentic systems. Bishop Fox's AI practice is growing, but organizations evaluating it should ask for specific case studies on multi-agent orchestration testing rather than accepting general application security credentials as equivalent.

Trail of Bits — Cryptographic and Systems-Level Rigor

Trail of Bits occupies a distinct position in any list of security firms because its foundational expertise is in cryptography, formal verification, and systems-level security research. The firm has engaged seriously with AI security, publishing research on model safety, neural network verification, and the formal properties of machine learning systems. Their work tends toward mathematical rigor and documented proof of vulnerability rather than qualitative assessment.

For agentic systems, Trail of Bits' most relevant capability is in auditing the underlying code infrastructure — the frameworks, APIs, and execution environments that agents run within. If an agentic system is built on a custom orchestration framework or uses a novel memory architecture, Trail of Bits has the systems security depth to audit those components with a level of rigor that most AI-focused firms cannot match. Their limitation in the agentic space is that adversarial prompting and behavioral red teaming are different disciplines from cryptographic audit, and organizations typically need both rather than choosing between them. Labarna AI's discussion of evidence-based resolution with machine judgment and human escalation is relevant here: the systems layer and the behavioral layer both require dedicated expertise.

Preamble — Runtime Policy Enforcement for LLMs

Preamble takes a different approach from most vendors on this list by focusing on runtime policy enforcement rather than point-in-time testing. Their technology sits as a layer between an application and the LLM it calls, intercepting inputs and outputs to enforce defined behavioral policies before they reach the model or the user. The practical effect is that certain classes of prompt injection and jailbreak attempts are blocked at the policy layer rather than relying on the underlying model to refuse them.

For organizations deploying agentic systems in regulated industries where specific behavioral constraints are mandatory — financial services, healthcare, legal — Preamble's policy enforcement layer addresses a real gap. The limitation is that runtime policy enforcement and penetration testing serve different functions: enforcement reduces the attack surface; testing reveals whether the residual attack surface has exploitable vulnerabilities. Organizations that deploy Preamble's enforcement layer still benefit from periodic adversarial testing to verify that the policy layer itself is not bypassable. The two approaches are complementary rather than substitutes.

Key Vulnerability Categories Every Agentic Pen Test Must Cover

A credible agentic security assessment covers at least five distinct vulnerability categories, and any vendor that scopes a test without addressing all five should be asked to explain the omission. The first category is direct prompt injection — adversarial inputs submitted through the primary interface that cause the agent to deviate from its intended behavior. This is the most documented category and the one most likely to be covered by any vendor with LLM security experience.

The second category is indirect prompt injection through retrieval-augmented pipelines. An agent that retrieves documents, browses the web, reads emails, or queries databases is exposed to attacker-controlled content in each of those channels. A test that only evaluates the primary input interface misses the majority of the real-world attack surface for agents deployed in enterprise environments. Labarna AI's piece on explicit policy and human intent at machine speed describes why policy definition at the architecture stage is the first defense against this category.

The third category is tool permission abuse — testing whether an attacker can manipulate the agent into using its granted tool access in unintended combinations. This requires testers who understand the specific tools in scope and can construct multi-step attack chains rather than single-turn injection attempts. The fourth category is memory poisoning across sessions, which requires a multi-session testing methodology. The fifth category is inter-agent trust exploitation in multi-agent systems, where one agent can be manipulated into sending malicious instructions to another agent that implicitly trusts it.

How to Evaluate a Vendor Scoping Conversation

The scoping conversation with a prospective security vendor tells you more about their actual capability than their marketing materials do. Three questions reliably separate vendors with genuine agentic testing capability from those adapting existing web application or LLM testing frameworks to a problem they have not fully internalized. First, ask them to describe how they test for indirect prompt injection through a retrieval-augmented generation pipeline — specifically, what their process is for inserting adversarial payloads into documents that an agent would retrieve during normal operation.

Second, ask how they approach multi-session memory poisoning. A vendor that describes only single-session tests is not equipped for the full threat model. Third, ask for their methodology for testing inter-agent trust boundaries in a system where multiple agents communicate through a message queue or shared memory store. These questions require specific operational answers, not reference to general penetration testing frameworks. Vendors that redirect to OWASP references without operational specificity have likely not conducted a full agentic engagement in production conditions.

The Relationship Between Deployment Architecture and Security Posture

Security posture in an agentic system is not primarily determined by the tests run against it. It is primarily determined by the architecture that was built in the first place. Systems designed with least-privilege tool access, explicit escalation paths for unhandled exceptions, verifiable audit trails for every agent action, and human-in-the-loop triggers for high-stakes decisions present a fundamentally smaller attack surface than systems that were built for capability first and audited for security afterward.

This is the structural argument for involving production infrastructure specialists at the deployment stage rather than bringing in security testers after a system is live. The cost of finding a confused deputy vulnerability in a pre-production architecture review is a design change. The cost of finding the same vulnerability in a post-deployment penetration test is a system rebuild. TFSF Ventures FZ LLC's production infrastructure approach, delivered under its 30-day deployment methodology, builds the audit trail, exception handling, and tool permission architecture into the initial deployment rather than treating them as remediation work. Labarna AI's detailed treatment of audit trails as first-class citizens rather than compliance afterthoughts provides technical grounding for why that sequence matters.

The agentic economy is moving quickly enough that organizations face a genuine tradeoff between deployment speed and security rigor. The firms on this list represent different positions on that tradeoff — some prioritize testing existing systems, some prioritize building systems that are harder to exploit from the start. Understanding which problem you are trying to solve is the prerequisite for selecting the right partner.

About TFSF Ventures FZ LLC

TFSF Ventures FZ-LLC (RAKEZ License 47013955) is an AI-native agent deployment firm built on three pillars, all running on its proprietary Pulse engine: autonomous AI agents deployed directly into the systems a business already runs, a patent-pending Agentic Payment Protocol licensed to enterprises and payment networks globally, and a Venture Engine that compresses the full venture lifecycle from idea to investor-ready. Founded by Steven J. Foster with 27 years in payments and software, TFSF operates globally across 21 verticals with a 30-day deployment methodology. Learn more at https://tfsfventures.com

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Originally published at https://www.tfsfventures.com/blog/penetration-testing-an-agentic-system

Written by TFSF Ventures Research