In Episode 83, titled “AI-Related Attacks (High-Level),” the most useful way to think about risk is surprisingly familiar: attackers manipulate inputs and they exploit data exposure. Even when the system feels new, the failure modes often rhyme with what you already know from application security, because the model sits inside a larger workflow that accepts requests, retrieves context, and produces outputs. When those inputs are untrusted and the data is valuable, you should assume adversaries will probe for ways to steer behavior and pull information out of the system. The challenge is that AI systems can be persuasive and flexible, so bad outcomes may look like “normal” responses until you recognize the pattern. A high-level map of these risks helps you classify issues quickly and communicate them clearly without getting lost in model internals.
Prompt injection is one of the simplest concepts to grasp because it is essentially crafted input that changes system behavior in an unintended way. The system is designed to follow instructions, but it cannot reliably distinguish between instructions that serve the user’s goal and instructions that subvert the system’s intended constraints. In practice, an attacker embeds directives inside text that the model is asked to process, hoping those directives override policies, reveal restricted information, or alter downstream actions. This becomes more dangerous when the model is connected to tools, retrieval systems, or workflows that can take real actions based on the output. The core idea is not that the model is “tricked like a human,” but that instruction-following is a feature that can be exploited when untrusted content is treated as authoritative guidance.
Data leakage risk is the second major pillar, and it is best understood as the system revealing information it should not reveal. In some deployments, the model has access to private context, such as internal documents, user data, configuration notes, or other retrieved content that is injected into the prompt to improve accuracy. If the system is not carefully designed, an attacker can coax the model into reproducing that context, summarizing it, or exposing pieces that were never meant for the requester. Leakage can also occur through logs, analytics, or debugging views that store prompts and outputs, effectively creating a sensitive transcript of interactions. The risk is not limited to dramatic “dump the database” moments, because small leaks accumulate, and even partial disclosures can be enough to confirm the presence of specific data or enable targeted follow-on attacks. When you assess leakage, you focus on what sensitive sources exist, how they are introduced to the model, and how outputs can inadvertently carry them back out.
Model manipulation at a high level is about steering outputs toward harmful outcomes, even when the model is not supposed to behave that way. Sometimes this looks like pushing the model to adopt a different role, to ignore constraints, or to prioritize an attacker’s objective over user safety or organizational policy. Other times it is more subtle, such as shaping responses so they are consistently biased toward a particular conclusion, recommending unsafe actions, or misclassifying information in a way that benefits the attacker. This risk becomes especially relevant when model outputs are treated as authoritative guidance for decisions, triage, or automated workflows. The attacker’s goal is not always to make the system obviously “bad,” but to make it reliably wrong in a direction that helps them. Thinking in those terms keeps you grounded in outcomes and impact rather than in abstract debates about model intelligence.
Supply chain risk shows up when you consider where the model and its components came from and what assumptions you are making about trust. Many AI systems rely on third-party models, embedding services, libraries, plugins, retrieval pipelines, or fine-tuned artifacts that were not built under your organization’s control. If any of those components are untrusted, compromised, or simply poorly governed, hidden behavior can be introduced that is difficult to detect during routine functional testing. This can include backdoors in model behavior, malicious code in supporting packages, or poisoned data in model updates that changes how the system responds to certain triggers. The practical lesson is that you should treat models and their dependencies like any other critical software supply chain, with provenance, integrity checks, and controlled update paths. When supply chain governance is weak, you may be relying on a black box that can change underneath you in ways that undermine security and auditability.
Access control risks deserve special attention because AI systems often broaden who can query capabilities and what those queries can retrieve. You are not just controlling access to a webpage or an API endpoint, you are controlling access to a system that can synthesize answers from sensitive sources and present them in an accessible way. If authentication is weak, authorization rules are coarse, or tenant boundaries are unclear, users may be able to retrieve information they are not entitled to see simply by asking in the right way. Even with strong identity, you still need to consider what the model can access on behalf of the user, including internal documents, ticket systems, code repositories, or customer records. The risk is magnified when the model is integrated into workflows that expose data through summaries, explanations, or troubleshooting guidance that inadvertently includes private identifiers. Effective access control in AI is about limiting both who can ask and what the system can “see” while answering.
Logging matters because prompts and outputs can be sensitive data in their own right, not just operational metadata. Prompts can contain proprietary questions, incident details, customer information, or confidential instructions, and outputs can contain derived summaries that are still sensitive or even more revealing than the original query. If logs are broadly accessible, retained too long, or shipped to third-party observability platforms without proper controls, the organization can create a new repository of high-value information that attackers will target. Logging also intersects with compliance, because retention and access policies may need to align with data protection requirements, particularly if users are submitting personal or regulated data. At the same time, logging is essential for detecting abuse, investigating incidents, and improving controls, so the answer is not “don’t log,” but “log intentionally.” The security mindset is to treat AI interaction logs as sensitive assets with least privilege, minimization, and clear retention boundaries.
A practical scenario helps tie these concepts together, such as an assistant that reveals private instructions or data when pressed with carefully crafted requests. Imagine a system that uses internal prompt instructions to enforce policy and uses retrieved documents to answer helpdesk questions faster. An attacker interacts with it repeatedly, probing for phrases that cause it to disclose hidden guidance, internal configuration notes, or snippets of retrieved content that were meant to remain behind the scenes. Over time, the attacker builds a clearer picture of what the assistant is told to do, what sources it can access, and where the guardrails are weak. The outcome might be a direct disclosure of sensitive data, or it might be a disclosure of internal policy text that helps the attacker design better bypass attempts. This illustrates why “private instructions” and “context data” should be treated as sensitive, because exposure can enable both immediate harm and more effective future attacks.
Safe validation in that scenario means demonstrating the risky behavior without turning the test into a mass disclosure event. The objective is to prove that the system can be induced to reveal restricted content or to cross an access boundary, while keeping the exposed material tightly controlled and minimized. A responsible approach uses non-sensitive placeholders when possible, limits the amount of output captured, and focuses on showing the mechanism of leakage rather than collecting a large quantity of confidential data. You want your evidence to be sufficient to support remediation, which usually means capturing the triggering prompt pattern, the fact that restricted content appeared, and the conditions under which it happened. You also want to avoid creating additional risk by spreading sensitive outputs through screenshots, shared channels, or broad reports. The professional standard is to demonstrate impact clearly while practicing restraint, because the goal is risk reduction, not proof through excess.
Mitigation concepts at a high level can be organized around input controls, output filtering, and strong access boundaries, with the understanding that none of these is a single magic fix. Input controls include treating user input and retrieved content as untrusted, constraining how instructions are interpreted, and preventing untrusted text from overriding system policy. Output filtering focuses on preventing the model from emitting secrets, regulated data, or policy-restricted content, and it works best when paired with clear definitions of what must never leave the system. Strong access boundaries require tight authorization on retrieval sources, tenant separation, and careful control over what tools or data sources the model can reach on a user’s behalf. The most effective mitigations recognize that the model is part of an application, and application security principles still apply: validate inputs, enforce authorization, minimize sensitive exposure, and monitor for abuse. When you speak in these terms, you make AI risk actionable for security teams who already know how to build defenses, even if the interface is novel.
A common pitfall is assuming AI errors are harmless or purely quality issues, like a chatbot occasionally giving an odd answer. In security terms, an error that reveals a secret is not a “bad answer,” it is a confidentiality breach, and an answer that triggers an unsafe action in a workflow is not a “mistake,” it is a control failure. Another dangerous assumption is that because the model is probabilistic, you cannot hold it to security requirements, which can lead to vague ownership and weak accountability. Attackers thrive in that ambiguity because it creates a space where no one is sure what the system is allowed to do, what it actually does, and who is responsible when it misbehaves. It is also easy to underestimate incremental leakage, where each response reveals a small clue that becomes significant when combined across many interactions. Treating these as real security outcomes rather than mere “AI quirks” is a key shift in thinking.
Another pitfall is focusing on the model alone and ignoring the surrounding system that often creates the real exposure. Retrieval mechanisms, prompt templates, tool integrations, and data connectors are frequently where boundaries are enforced, and weak enforcement there can make the model look like the culprit when the application design is the true root cause. If a model can access a sensitive repository without strict authorization checks, the model may faithfully summarize data it should never have been given, and the security flaw is the retrieval boundary. If logs store full prompts and outputs in a widely accessible system, leakage may happen even if the model’s visible responses are carefully filtered. In assessments, you want to map how data flows through the system: where it is sourced, how it is scoped to a user, how it is transformed, and where it is stored. That broader view prevents shallow conclusions and produces mitigations that actually reduce risk.
Quick wins are usually about reducing sensitive context and enforcing audit trails, because those changes often deliver immediate risk reduction without requiring a full redesign. Limiting sensitive context means minimizing what the model can see by default, scoping retrieval to only what the user is authorized to access, and avoiding injecting secrets or internal policy text into prompts unless it is strictly necessary. Enforcing audit trails means capturing enough telemetry to investigate abuse while still protecting the logs as sensitive assets, including identity attribution, rate limiting signals, and anomaly detection for suspicious patterns of querying. You can also tighten operational controls around who can access administrative interfaces, prompt templates, and configuration, because those are often high-impact levers that attackers would love to influence. These improvements are not glamorous, but they shrink the attack surface quickly and make the system easier to govern. When you prioritize quick wins, you buy time to implement deeper mitigations with less pressure and fewer unknowns.
To keep the core themes in your head, use a simple memory anchor: input, data, behavior, supply chain, access. Input reminds you that crafted requests and untrusted content can steer the system, especially when instruction-following is treated as a universal feature instead of a controlled capability. Data points you to leakage risks, both from retrieved context and from the operational trail created by prompts and outputs. Behavior helps you remember that the attacker’s goal may be to nudge outcomes, not just to extract secrets, particularly when outputs influence decisions or automated actions. Supply chain forces you to ask what you are trusting, how components are sourced, and how updates are controlled, because hidden behavior can enter through dependencies. Access anchors governance and authorization, ensuring that who can query the system and what it can retrieve are treated as hard security boundaries rather than convenience features.
As we wrap up Episode 83, the big picture is that AI-related attacks often reduce to familiar security themes expressed through a new interface: input manipulation, data exposure, and boundary failures that enable both. The strongest posture treats the AI system as part of a larger application and applies disciplined controls around what it can see, what it can do, and what it can say. If you had to add one control to improve security in a typical assistant deployment, a high-impact choice would be strict authorization and scoping on retrieval, ensuring the model only receives context that the requester is entitled to access and only from approved sources. That single boundary sharply reduces the damage of prompt injection attempts because there is less sensitive material available to leak in the first place. When you can articulate one concrete control like that, you demonstrate the kind of risk-based thinking PenTest+ expects and the kind of practical governance AI deployments actually need.