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Some LLM deployments are approved without anyone being able to explain (in concrete terms) how the system behaves when it is pushed off the happy path. This is a direct result of applying traditional AppSec and cloud review models to systems that do not behave deterministically.
Security sign-off often means the architecture looked reasonable, access controls existed, and data flows were documented, not that the model was tested against the ways it can actually be abused.
This is why so many teams are more exposed than they realize. LLM-powered applications introduce failure modes that standard reviews do not meaningfully examine, including prompt injection, unintended data disclosure through context windows, unsafe tool invocation, and outputs that cannot be bounded or predicted with confidence. Yet many organizations still treat these risks as theoretical, or worse, assume that a vendor model or a basic AI policy somehow absorbs the responsibility.
What makes this situation more dangerous is the confidence that comes from shallow reviews. A one-time assessment creates the impression of control while prompts evolve, models change, integrations expand, and data sources shift quietly in the background. Security teams then face the worst possible position after an incident, knowing something was reviewed, but unable to prove that the review addressed the real risks or kept pace with how the system actually operates.
Most LLM security assessments fall apart because the team cannot clearly describe the system under review. People say “the AI feature” as though that means something concrete, yet no one can point to a defined architecture and explain how it works end to end. When the system itself is fuzzy, every risk discussion that follows becomes vague by default.
Risk does not exist in an abstract capability. It exists in deployed systems with real inputs, real outputs, and real dependencies. Until you describe those clearly, there is nothing meaningful to assess.
An LLM cannot be evaluated in isolation because its behavior is shaped by where it runs and how it is used. A model exposed through a SaaS API has a different risk profile than one running as an internal shared service. An embedded agent with tool access behaves very differently from a simple text generation endpoint. These distinctions drive data exposure, control boundaries, and failure impact, and they must be explicit.
Start by locking down where the LLM sits in your architecture. At a minimum, the assessment scope should clearly state:
This level of clarity prevents teams from talking past each other and forces alignment on what is actually in scope.
LLM behavior is driven by everything that feeds into it instead of what a user types into a chat box. Assessments that stop at user input miss the majority of the exposure. You need a complete view of all inputs that influence model behavior, including:
Each input source introduces its own risks, from prompt injection and data poisoning to unintended disclosure of sensitive internal information. Treating all inputs as equivalent is how critical attack paths stay hidden.
Outputs are often discussed in terms of accuracy or quality, yet from a security perspective, impact matters far more than correctness. An LLM that returns text to a user is one thing. An LLM whose output is stored, forwarded, or acted upon is something else entirely. Your assessment should explicitly document:
This distinction is critical once outputs influence workflows, automation, or decision-making, because a single unsafe response can propagate far beyond the original interaction.
Teams focus on the model itself and gloss over the components that actually control how it behaves. These parts often sit outside traditional AppSec reviews, yet they define most real-world failure modes. A thorough assessment should explicitly call out and include:
Each of these layers can be manipulated, misconfigured, or drift over time. Ignoring them because they feel like implementation details creates a false sense of coverage.
Defining the system clearly is not busywork. It sets hard boundaries around what is being assessed and makes it far more difficult to miss meaningful attack paths. For CISOs and security leaders, this approach creates a consistent way to scope LLM security assessments across teams and products, without relying on vague statements about “using AI.”
Once everyone is looking at the same concrete system, risk discussions become grounded, repeatable, and defensible. Without that foundation, every assessment is just an opinion dressed up as analysis.
We look at LLM threats and mentally map them to familiar web issues, then assume the same controls and review habits will work. That assumption is exactly how risk gets underestimated. The most damaging failures usually happen in the interaction layer, where user input, prompt construction, retrieved context, tool permissions, and model behavior collide in ways your existing scanners and review checklists were never built to reason about.
Traditional AppSec is great at finding known classes of bugs in deterministic code paths. LLM systems create probabilistic behavior that you still have to secure, and the weak point is often not the model itself but the way you assemble instructions and allow outputs to influence actions.
Below are the threat categories that deserve explicit assessment in any enterprise LLM application. These are not superficial, and they show up even in simple chat-style deployments once you connect the model to internal data, workflows, or tools.
Prompt injection is an attacker using inputs to manipulate the model’s instruction hierarchy, causing it to ignore system rules, developer intent, or safety constraints. The core issue is that the model treats untrusted text as something it should reason about, and many implementations feed attacker-controlled content directly into the same context window as trusted instructions. What to assess:
The most common LLM security failure is not model compromise. It is the model revealing data it was never supposed to reveal, because sensitive context was placed into the prompt and the model was asked questions that caused it to surface that context. What to assess:
Multi-tenant LLM applications can leak data across tenants through flawed retrieval scoping, caching, indexing mistakes, prompt assembly bugs, or shared conversation memory. This is one of the fastest ways to turn an AI feature into an incident that triggers contractual and regulatory escalation. What to assess:
Tool calling moves risk from bad text output to model-triggered actions. When the model can call functions, query internal systems, send emails, create tickets, approve workflows, or run scripts, prompt injection becomes a path to operational impact. What to assess:
Even when data isolation is strong, attackers can still use your model for activities your business never intended to provide, such as generating phishing content, automating social engineering at scale, or producing prohibited content. This becomes a governance, abuse, and cost problem, not just a security bug. What to assess:
This category is where many security leaders get blindsided, because the vulnerability is not a classic exploit. It is misplaced trust. When downstream systems treat model output as authoritative, the model can become a decision engine without the controls that real decision engines require. What to assess:
This is where teams get frustrated, because they did the right security steps and still missed the real exposure. A few common failure points show up repeatedly.
This threat lens helps you stop underestimating AI risk just because the words sound familiar. You get a practical set of categories that map directly to how enterprise LLM systems fail, which means your teams can assess risk based on real interaction paths instead of recycling web-app checklists. Once you use these categories consistently across products and teams, you get clearer scoping, better findings, and far less false confidence from reviews that were never designed for LLM behavior.
LLM systems quietly expand the blast radius of sensitive data because they pull context from more places, persist more artifacts by default, and create more copies of data than traditional applications do. You cannot call your assessment complete until you have traced where sensitive data can flow, where it can land, and who can get it back out later.
The model is only one stop in the lifecycle. Data touches ingestion pipelines, retrieval systems, logging layers, analytics tooling, and sometimes training or fine-tuning workflows. Each step can create new persistence, new access paths, and new compliance obligations.
You want a concrete map that shows every place sensitive data can enter the LLM system, every transformation step, and every place it can be stored or reused. Start with these common flows, then extend them based on your architecture.
A lot of we do not store customer data claims fall apart here, because teams are looking at the primary database and forgetting that prompts, embeddings, and traces often live somewhere else with different retention and access controls.
Once you have the flows, you need assessment questions that force real answers. These are the questions that stop teams from relying on vendor marketing language or vague internal assumptions.
These questions usually surface the uncomfortable truth: LLM systems often turn data access into data access plus derived access plus log access, and the controls rarely keep up.
Many teams treat embeddings as harmless because they are not plaintext, and that assumption is risky. Embeddings are derived from sensitive source material, they can leak information through retrieval behavior, and they often get broader access because they live in data infrastructure rather than application infrastructure. Treat embeddings, prompt logs, and telemetry as sensitive by default, then apply least privilege and retention rules that match the sensitivity of the underlying data. Practical controls to validate during assessment:
This is where security and legal exposure shows up fast, because LLM data flows are easy to misrepresent accidentally. Even when you think you have the right controls, the lifecycle can break your commitments through default retention, cross-region processing, or reuse in training and evaluation. Key implications to pressure-test:
Security leaders should treat we can delete it as a claim that requires operational evidence. You want to see the actual deletion workflow, the systems it touches, the exceptions, and the audit trail that proves it happened.
This approach gives you a defensible way to evaluate AI data exposure without trusting vendor assurances or internal optimism. You walk away with a repeatable framework that forces visibility into what data reaches the model, what gets stored by default, and who can retrieve it across the entire lifecycle. That is how you avoid accidental policy violations, prevent quiet compliance drift, and stay ready for the questions that show up after an incident or during an audit.
Security assessments go off the rails when they turn into a control inventory. Anyone can list guardrails, moderation, RBAC” and monitoring in a doc. The hard part is proving those controls hold up when the model gets hostile inputs, messy context, and real operational pressure. LLMs behave differently than traditional software under stress because the failure modes are interaction-driven, and controls that look solid on paper often degrade quietly when prompts, retrieval context, and tool calls start interacting.
Static reviews are inadequate here because they confirm presence, instead of effectiveness. They verify that a filter exists, that a policy exists, that a rate limit exists, then they stop. In an LLM system, you need evidence that the control still works across realistic adversarial scenarios, across the paths that matter, and across the system boundary you defined earlier.
A practical assessment approach treats every control as a claim that you can validate with targeted tests and observable outcomes. When teams skip this step, they end up with controls that reduce risk in theory and fail in production, or worse, controls that create confidence while attackers walk right around them.
Here are the control categories that deserve explicit testing in LLM applications, along with what working actually means.
Prompt hardening is usually described as having system instructions, sanitizing prompts, or blocking dangerous strings. None of that proves the model will follow constraints once untrusted content lands in the same context window as trusted instructions. You want proof that the system resists instruction override and that prompt construction does not accidentally give attackers influence over the instruction hierarchy. Testable checks that matter:
Output filtering often exists, and it often fails in the exact cases you care about because model output is variable and context-dependent. The goal is not to show that a filter runs, but to show that it consistently prevents sensitive disclosure and unsafe actions across the outputs your system actually produces. Validate effectiveness across:
A strong assessment includes measurable outcomes, such as leakage rate under test prompts, false negative patterns, false positive impact on business workflows, and which output channels bypass filtering (API responses, logs, stored transcripts, downstream queues).
Traditional rate limiting focuses on request volume. LLM abuse often shows up as pattern abuse, such as repeated extraction attempts, systematic prompt injection probing, automation at scale, or tool invocation fishing. A working control set detects and slows the behavior that indicates intent, even when request rates look normal. Assess whether you can:
Once the model can call tools or functions, authorization becomes the control that separates bad output from real damage. Too many systems treat tool calling as a feature layer, then forget that tool calls are privileged operations with real security requirements. You need to know which identity performs the action, which permissions apply, and how user intent is enforced. Controls to validate through testing and review:
Observability alone is not monitoring. Many teams have dashboards and logs, yet they cannot detect the behaviors that indicate security failure in LLM systems. You want monitoring that answers security questions quickly, such as whether users are probing for secrets, whether retrieval is returning cross-tenant content, or whether tool calls are being triggered unexpectedly. Look for monitoring coverage across:
A defensible LLM assessment includes adversarial testing because paper reviews cannot predict how the system responds to hostile prompts and hostile context. This does not need to be chaotic or unbounded, it needs to be structured and repeatable so you can show evidence and track improvement over time. A practical adversarial test set should include:
Run these tests against the full pipeline, not just the model endpoint, because the risk lives in orchestration, retrieval, tool execution, and downstream consumption.
When you focus on effectiveness, the assessment stops being a control checklist and starts producing evidence. You can point to tests, results, failure modes, mitigations, and retests, and you can show how the system behaves under pressure across the paths that matter. That gives security leaders a defensible answer to the question everyone asks after an incident, which is how you know it is safe enough to ship and safe enough to keep running as the system changes.
LLM risk only becomes actionable when you tie it to business outcomes that leadership already cares about, and when someone owns the risk in a way that survives roadmap pressure. A risk register full of vague statements like prompt injection possible or LLM may hallucinate does not help you prioritize, fund mitigations, or defend decisions later. Ownership matters just as much, because unowned risk does not get managed, it gets debated until the next incident forces an answer.
Your assessment findings should land in the language of impact, instead of just the language of mechanisms. That does not mean dumbing it down, but connecting the failure mode to what it breaks, who it affects, and what it costs. The same technical issue can be a nuisance in one workflow and a major incident in another, so the mapping needs to be specific to the system boundary and use case.
Here are common LLM failure modes and how they translate into executive-level outcomes:
To keep this grounded, tie each mapping to a concrete impact path in your architecture. That means documenting which data is at risk, which system or decision gets affected, which users get hit, and what the operational blast radius looks like during response and containment.
Security teams get pulled into debates about whether a threat is realistic. The right question is whether the impact path exists and whether the controls can withstand realistic pressure. A low-sophistication prompt injection attempt becomes high impact the moment it can reach sensitive RAG sources, trigger privileged tool calls, or influence an automated decision flow. That is why prioritization should key off factors you can defend:
The fastest way to stall remediation is to let ownership stay vague. Shared responsibility sounds collaborative, but it often becomes an excuse for nobody to act because everyone is waiting for someone else to define the fix. Clear ownership does not mean one team does everything, it means each part of the problem has a named owner with authority, budget influence, and accountability. A practical ownership model that holds up in enterprise environments looks like this:
This structure works because it matches accountability to leverage. Product can shape behavior, engineering can enforce controls, and security can validate outcomes and provide defensible evidence.
Once you map risks to impact and lock in ownership, communication gets easier because you stop presenting AI risk as a vague category. You can say which business outcomes are at risk, what the most likely failure paths look like in your environment, which controls reduce that risk, and who is responsible for maintaining those controls as the system evolves. That is how you move from blanket risk statements to prioritization and governance, and that is also how you answer the question leadership will ask in plain terms, which is who owns this risk and how you know it is being managed.
A one-and-done security assessment does not survive contact with a real LLM deployment. These systems change faster than traditional software because teams iterate on prompts, swap models, add new data sources, and expand use cases without treating those changes as security-relevant. The uncomfortable reality is that a static assessment becomes obsolete quickly, sometimes within days, because the behavior you assessed is no longer the behavior you are running.
This is an operational risk problem. LLM security lives in configuration and interaction paths, and those evolve constantly. When your assessment cadence looks like annual or quarterly reviews, you create long windows where risk drifts quietly and nobody notices until something goes wrong.
You do not need to reassess on every minor code change, but you do need clear triggers that reliably capture behavior changes and exposure expansion. These triggers should be explicit, measurable, and tied to your delivery process so teams cannot forget to bring security back in.
At minimum, reassessment should trigger when any of the following changes occur:
These triggers need to be treated as security-relevant by default because they change how the system behaves, what it can access, and what it can leak or misuse.
To operationalize LLM security, you want the work to happen where change happens, which is design, development, and runtime. This is where most programs struggle because they try to bolt continuous security onto a process that was built for periodic reviews. The fix is to embed lightweight, repeatable checkpoints into the workflow so reassessment becomes normal engineering behavior.
Design-stage reviews are where you catch the decisions that create irreversible risk later, such as which data sources can be retrieved, what tool permissions exist, and whether outputs drive decisions or actions. These reviews should focus on the specific changes that trigger reassessment, not a full re-review of the entire system every time. A design-stage reassessment should confirm:
Most teams already have changed control for code. LLM systems need change control for prompts, orchestration configs, retrieval connectors, and tool schemas, because those are security-critical. You want CI/CD to surface these changes automatically and route them into the reassessment workflow. Practical CI/CD gates that scale:
This works best when prompts and orchestrator logic are treated as versioned artifacts, stored with code, reviewed like code, and tested like code.
Continuous monitoring matters because some failures only show up under real usage patterns. You are not looking for generic model quality metrics, but for security signals that indicate probing, leakage, misuse, or drift. This monitoring should feed back into reassessment triggers, so you can respond to real-world signals instead of waiting for a calendar reminder. Monitoring that actually supports security:
The key is to connect these signals to action. Monitoring without escalation paths, ownership, and retesting turns into dashboard theater.
Annual reviews assume that the system stays stable between review points and that risk changes are slow and visible. LLM systems violate both assumptions because behavior changes through prompts, data sources, and model updates that are easy to ship and hard to reason about after the fact. When your assessment is annual, you end up defending decisions about a system that no longer exists in the form you assessed, and that is a bad place to be during audits, customer escalations, or incident response.
A continuous assessment model turns LLM security into an operating discipline instead of a compliance event. You get predictable triggers, repeatable reassessment workflows, and runtime signals that tell you when reality diverges from assumptions. That makes it possible to scale assessments across teams without scaling headcount at the same rate, because you stop relying on manual one-off reviews and start relying on structured change control, targeted testing, and monitoring tied to security outcomes.
The biggest mistake you can make with LLM security is treating it as a special case that will eventually settle down. It will not. The systems are getting more capable, more connected, and more embedded in decision-making, which means small design shortcuts today become hard-to-defend risks tomorrow. The danger is not that teams ignore security, it is that they overestimate how much their existing processes still apply.
This is an opportunity to reset how AI risk is handled across the organization. Teams that treat LLM security as an engineering discipline, with clear boundaries, continuous assessment, and defensible outcomes, will move faster with fewer surprises. Teams that treat it as a compliance exercise will spend more time explaining incidents than preventing them.
If you want to take this from guidance to execution, this is where we45 fits naturally. We work with security and product teams to assess real GenAI systems, pressure-test controls, and build defensible AI security programs that hold up under audit, incident response, and board scrutiny. When you are ready for that next conversation, start by looking at we45’s AI security services and see how they apply to the systems you are running today.
The riskiest components are often the least visible implementation details that control model behavior, not the model itself. These include: Prompt templates and system instructions. Orchestration layers that control sequencing and decision logic. Retrieval-Augmented Generation (RAG) pipelines that pull data from internal sources. Tool or function calling mechanisms. Guardrails, filters, and post-processing logic applied to outputs.
Outputs are critical because they determine impact, which is more important than correctness from a security perspective. An LLM output that is acted upon (stored, forwarded, or used to influence workflows/automation/decision-making) is far riskier than one that simply returns text to a user. A single unsafe response can propagate beyond the initial interaction.
Traditional input validation, such as sanitizing user input or filtering special characters, is ineffective against prompt injection. This is because the LLM interprets meaning, not syntax. The real control point is how you structurally separate trusted instructions (system prompts) from untrusted content (user input, retrieved data) and how you constrain the tool actions and output handling.
Both embeddings and prompt logs must be treated as first-class sensitive assets. Embeddings are derived from sensitive source material and can leak information through retrieval behavior, often having broader access because they reside in data infrastructure, not application infrastructure. Prompt logs and telemetry frequently store raw prompts and completions, turning a single leakage event into durable exposure that can be retrieved by multiple internal teams.
Unauthorized tool execution moves the risk from bad text output to model-triggered actions with real operational impact. When a model can call functions, query internal systems, or approve workflows, a prompt injection attack becomes a path to: Unauthorized changes in systems of record. Data modification or deletion. Fraudulent transactions. Clean-up work that resembles an insider attack.
A one-time assessment is insufficient because LLM systems are constantly changing. Reassessment must be triggered when any of the following occur: Prompt changes: Updates to system prompts, templates, or orchestration logic. Model swaps: New base model, version upgrades, or new provider configurations. New data sources: Additional RAG connectors, expanded indexing scope, or new external APIs. Expanded use cases: New workflows, increased automation, or higher-impact decisions influenced by the model. Operational changes: Changes to logging, retention, caching layers, or analytics pipelines that store prompts or outputs.
Clear ownership prevents risk from being debated or ignored. The practical model divides accountability based on leverage: Product: Owns the feature's behavior, acceptable use, user experience choices, and customer commitments. Engineering: Owns the implementation, including prompt construction, tool boundaries, tenant isolation, secure defaults, and operational controls like rate limiting. Security: Owns the assurance, validation, assessment methodology, adversarial testing, and go-live criteria tied to continuous reassessment.
Traditional AppSec excels at finding known bugs in deterministic code paths. LLM security assessments must address probabilistic behavior and focus on interaction failures where user input, retrieved context, and model instructions collide. The weak point is often how instructions are assembled and how outputs influence actions, not just the model code itself.
Key LLM-specific threats include Prompt Injection and Instruction Override, Data Exfiltration through model responses, Cross-Tenant Data Leakage, Unauthorized Tool or Function Execution, Model Abuse for unintended tasks, and Output Trust and Downstream Decision Risk. These threats are driven by the model's reliance on untrusted input and its ability to trigger actions.
An LLM cannot be evaluated in isolation. The assessment scope must clearly define the architecture, including whether the model is a managed service or self-hosted, which teams own configuration and access controls, and how requests reach the model. It must also include a complete view of all inputs that influence model behavior, not just direct user input.
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