High-Risk AI Systems: How to Identify Them
Decisions made by high-risk AI systems can significantly affect people's safety, rights, or access to essential services, making early identification and strong governance critical.
Fergal Glynn
Large language models (LLMs) are increasingly integrated into real workflows, such as customer support, coding, research, internal search, and agentic automation. The problem is that the same behavior that makes LLMs useful also makes them easy to manipulate, especially when LLM guardrails rely on model-internal instructions rather than system-level enforcement.
Prompt injection attacks exploit that weakness. Attackers hide instructions in plain sight, and the model follows them. Sometimes, the injection is typed directly into a chat. Other times, it arrives indirectly through documents, web pages, Retrieval Augmented Generation (RAG) content, tool output, or memory.
Either way, the model has no built-in concept of trust. It treats all text as a single stream.
This article breaks down what a prompt injection attack is, how it works, why the risk runs deeper than a bad prompt, and which defensive controls actually help when LLMs connect to data and tools.
A prompt injection attack happens when someone tricks a language model into following instructions it was never meant to follow. The attacker sneaks these instructions into the model’s input so the model treats them like legitimate commands.
Prompt injection attacks can force a model to ignore rules, reveal sensitive information, change its behavior, or trigger actions through connected tools and APIs. Some attacks are obvious and typed directly into a chat box. Others are hidden inside the content the model is asked to read or summarize, making them much more difficult to detect.
These attacks are a consequence of how language models process text. Any system that feeds untrusted content into an LLM without strong controls is exposed by default.
Prompt injection attacks exploit how language models read and follow text. To understand why these attacks work so well, it helps to look at how LLMs process instructions, where trust breaks down, and how that differs from traditional injection flaws.
Most prompt injection attacks follow the same pattern.
The model doesn’t know that it was attacked. From its perspective, it simply followed instructions provided via text.
Injection is a familiar term in the cybersecurity community, but prompt injection behaves differently from classic injection flaws.
For example, SQL injection exploits a parser by breaking syntax rules. Prompt injection exploits instruction-following behavior. There’s no syntax error to detect and no query to escape.
Traditional injections aim for code execution, while prompt injections aim for instruction execution. The attacker doesn’t need to run code; they just need the model to comply.
Prompt injection isn’t a bug that you patch once. The problem is deeply embedded in how language models work.
Models process tasks as tokens, but they don’t understand where instructions come from. Instructions can come from:
All of it becomes the same stream of tokens. There is no native concept of trusted vs. untrusted data. Security boundaries exist outside the model, not within it.
Mindgard has analyzed Sora system prompt structures and identified cases where deeply embedded system-level instructions remain observable or influenceable despite being hidden. This behavior highlights how attackers can exploit prompt ambiguity to blur the line between system intent and untrusted output.
LLMs don’t follow rules; they predict the likely next tokens. System messages and safety rules usually win, but not always. Safety rules usually hold, but it isn’t guaranteed.
The model weighs patterns and context, but a cleverly written prompt can still override intent. That uncertainty is the attack surface.
Everything in the context window competes for influence:
Once merged, the model cannot reliably determine which text should control behavior. An injected instruction in a document can influence output just like a user command.
Longer context windows increase capability, but they also increase risk.
Alignment reduces bad behavior, but it does not fully eliminate it. Fine-tuning and reinforcement learning from human feedback (RLHF) shape tendencies, but they don’t enforce hard constraints.
There’s no definitive proof that a model will always refuse a malicious instruction, just evidence that it often does. Security teams should treat this as a risk-reduction measure rather than a prevention measure.
Stronger models follow complex instructions better, including malicious ones.
Improvements in reasoning and autonomy expand the attack surface. Agentic behavior makes prompt injection more impactful. Every gain in capability comes with safety tradeoffs.
These challenges are why no LLM is fully immune to prompt injection. It should be treated as a systemic risk instead of a misuse edge case.
Prompt injection shows up in a few distinct forms. Each one exploits a different failure point in how LLM systems handle instructions, context, and authority. The defenses also vary. One-size-fits-all controls aren’t sufficient for preventing prompt injections.
Direct prompt injection happens when an attacker types instructions straight into a chat interface. The goal is to override rules and take control of the model’s behavior.
Defensive Controls:
Indirect prompt injection hides instructions inside the content that the model is asked to read, such as:
In indirect prompt injection attacks, the attacker never communicates directly with the model.
Defensive Controls:
Data source injection targets systems that ingest data at scale, whether from external or internal sources. The attacker poisons a source, causing malicious instructions to be automatically pulled into prompts.
Defensive Controls:
Multi-hop injection exploits agent workflows that operate across multiple steps. An instruction model introduced early quietly reshapes behavior later, after the original context fades.
Defensive Controls:
Generative prompt injection occurs when the model generates its own instructions and treats them as authoritative. This shows up in self-refinement loops, memory updates, and agent planning.
Defensive Controls:
Each of these attacks exploits the same underlying issue: models follow text without understanding trust. The controls that work assume that nothing inside the model can be trusted by default.
The table below highlights the different types of prompt injection attacks, how each type works, its primary failure point, and defensive controls to help prevent it.
Some prompt injection attacks don’t attempt to override rules directly. Instead, they slip past controls by manipulating context, framing, or authority. These variants demonstrate how attackers can bypass control logic once the model is embedded in a workflow.
Jailbreaking pressures the model to abandon restrictions through persistence, framing, or gradual escalation. The attacker reshapes the conversation until the model treats unsafe behavior as acceptable.
Defensive Controls:
Role-play manipulation assigns the model a fictional role with implied authority. Once the model accepts that role, it may justify actions that could otherwise be blocked.
Defensive Controls:
In this variant, the model generates new instructions and then follows them. The danger comes from treating model-authored content as trusted.
Defensive Controls:
Instruction shadowing happens when new instructions quietly take precedence over earlier rules. Nothing gets removed, but authority shifts.
Defensive Controls:
Conditional authority injection embeds statements like “If X, then you are allowed to do Y.” The model treats the condition as satisfied and escalates privileges.
Defensive Controls:
Attackers hide instructions inside translation, summarization, or formatting tasks. The transformed output is later reused as if it were safe.
Defensive Controls:
This is a specialized form of generative prompt injection that exploits agent planning and execution boundaries.
Instruction smuggling occurs when control logic appears during a model’s reasoning or planning steps rather than in the final response. The model might be asked to think through a plan, analyze options, or break a task into steps.
Within that reasoning, it provides instructions on what should happen next. When those intermediate steps are reused, the system feeds planning output directly into execution.
The model has effectively written its own executable instructions without review, authorization, or intent.
Defensive Controls:
The table below consolidates these bypass variants into a practical reference showing how each attack works, what it exploits, and which controls can contain it.
Prompt injection is rarely the end goal. Attackers use it to unlock real abuse paths inside AI-powered systems. Once control shifts, models can leak data, misuse tools, corrupt records, or quietly ignore policy.
Attackers coerce the model into revealing sensitive information. That can include internal documents, customer data, credentials, or proprietary context pulled in through retrieval.
When models can call tools or APIs, prompt injection turns into action. The model is coerced into invoking functions it should never touch or bypassing safety parameters.
Attackers inject instructions or false information into memory so that it persists across sessions. The model appears normal at first, then behaves differently over time.
In multi-agent systems, one compromised step can influence the next. A low-privilege agent injects instructions that later agents treat as trusted.
In unauthorized workflow execution, the model triggers actions outside the original task. That might mean sending messages, modifying records, or executing workflows that the user never approved.
The model appears compliant while quietly ignoring rules. Outputs look reasonable, but required constraints are skipped or altered.
Instead of stealing data, the attacker corrupts it. Model-generated changes discreetly alter authoritative records, analytics, or decisions.
These abuses only work when systems trust language too much. Effective defense assumes that any model output can be wrong, misleading, or hostile, even when it sounds confident.
Prompt injection defenses often fail because attackers don’t rely on obvious instructions. Evasion techniques focus on hiding malicious intent, so attacks bypass detection before control logic is even evaluated.
These techniques succeed because models read for meaning. Any defense that assumes clear or immediate attacks will miss most of them.
Real incidents make prompt injection easier to understand because they show how these failures appear in deployed systems. The following examples are based on behavior observed through Mindgard technology, where prompt injection or closely related control failures surfaced in production-grade models.
Direct prompt injection is the most straightforward case. An attacker gives the model explicit instructions to ignore rules or reveal restricted information.
This pattern is evident in early safety-bypass research and internal testing across many LLM deployments. The model complies because the instruction is presented in the same channel as trusted guidance, and it’s both recent and clearly worded. There’s no native mechanism inside the model to reject it based on authority.
On its own, this type of attack is obvious and easy to detect. It becomes more dangerous when paired with automation or tool access.
Mindgard technology observed interactions with Azure AI Content Safety, which provides a clear real-world example of indirect prompt injection.
In these cases, malicious or carefully crafted inputs were used to evade moderation and safety enforcement. The inputs were not framed as direct commands. Instead, they were embedded inside content that the model was expected to process normally.
The key failure was trust. Untrusted content was placed in the same context as policy-relevant instructions. Once inside, the model followed the semantic intent of the text, even when that intent conflicted with safety goals.
From the user’s perspective, nothing looked suspicious. The instruction was executed because the model treated all input as equally valid text.
Analysis performed by Mindgard technology on OpenAI Sora shows how generative prompt injection can lead to instruction leakage. In these cases, carefully shaped multimodal inputs caused the model to generate content that surfaced internal system instructions that were never meant to be visible to users.
This matters because the model was not directly told to reveal its system prompt. Instead, the behavior emerged during the generation process. The model reasoned its way into exposing internal control logic.
In automated systems, this risk scales fast. If a model can generate instructions about itself or its constraints, and those outputs feed into other steps, the model effectively becomes its own authority. That’s generative prompt injection in action.
Both the Azure and Sora examples show the same pattern.
Once models are embedded into automated pipelines, small failures compound. A single injected instruction can influence retrieval, summarization, tool calls, or agent planning. No human reviews each step.
The system trusts intermediate outputs by default. That trust is what attackers exploit.
Across these cases, payloads follow consistent design patterns:
As these examples illustrate, prompt injection shows up in real systems when models are trusted to interpret language without hard boundaries.
Prompt injection appears everywhere in AI-focused Capture the Flag challenges (CTFs). These challenges strip away theory and force people to confront how models actually behave under attack.
CTFs reveal how attackers think, rather than how defenders hope their models behave.
Capture-the-Flag challenges expose these cracks quickly.
Most AI CTF challenges revolve around a small set of realistic goals:
These scenarios mirror what happens in real deployments.
Capture the Flag challenges give security teams something that traditional testing often misses. They provide a safe environment to test real vulnerabilities. Teams can explore worst-case behavior without risking production systems.
CTF challenges give teams a better understanding of prompt injection mechanics, allowing engineers to see firsthand how subtle wording changes affect model behavior. They also train engineers to recognize payload patterns. Once someone has solved or failed a prompt injection CTF, injected instructions become much easier to spot in the wild.
Prompt injection rarely manifests as a single, obvious failure. Detection depends on spotting patterns that signal something is trying to cross a trust boundary. That means looking beyond raw inputs and paying attention to how behavior changes over time.
One of the clearest signals is an attempt to override or reframe authority.
Watch for language that claims higher priority than existing rules. Phrases that reference audits, policies, system instructions, or implicit approval often indicate delegated authority framing. Repeated attempts to restate or escalate instructions over multiple interactions are another red flag.
Prompt injection payloads often look strange once you know what to look for, such as:
All of these signals point to obfuscation. These payloads are designed to blend in while still being interpreted by the model as instructions.
MIndgard’s Offensive Security solution uses automated red teaming to generate realistic adversarial payloads. That includes obfuscated and encoded instructions designed to bypass filters. Testing against those payloads exposes weaknesses that static reviews miss.
A common failure mode happens when content crosses boundaries it should never cross. Examples include:
If analysis, summaries, or transformed content can influence actions, the boundary has already failed.
Mindgard’s AI Security Risk Discovery & Assessment identifies where untrusted data can flow into prompts, memory, tools, and agents. It uncovers how models behave, what they connect to, and where weaknesses emerge, surfacing exposures early and allowing security teams to make informed decisions.
Sometimes, the first signal that appears in the output. The model may justify actions it was never asked to take. It may reference rules it shouldn’t know about or quietly ignore constraints while producing plausible responses.
These anomalies matter even when the output looks reasonable. Silent non-compliance is often more dangerous than obvious failure.
Mindgard’s AI Artifact Scanning solution monitors runtime behavior and flags deviations between the intended policy and the model's actual behavior. That makes subtle failures visible instead of letting them slip through unnoticed.
Detection improves dramatically when systems are tested the way attackers think.
Adversarial testing reveals how models behave under pressure, and continuous monitoring catches drift as prompts, data sources, and workflows evolve. Without both, defenses slowly decay.
Mindgard’s Offensive Security solution continuously stress-tests AI systems using a large library of AI-specific attack techniques, including prompt injection variants. Combined with ongoing monitoring, teams can detect prompt injection attempts before they cause real damage.
Detection works best when language is treated as untrusted by default. The goal isn’t to guess intent but to make boundary failures visible early, before attackers gain leverage.
Prompt injection is a predictable failure mode that emerges whenever language models are placed in environments where untrusted text, automation, and authority intersect.
LLMs don’t understand trust. They don’t enforce instruction hierarchy, and they don’t reliably distinguish system intent from untrusted input. As models gain longer context windows, stronger reasoning, and more autonomy, the impact of prompt injection grows. Agentic workflows, RAG pipelines, tool calling, and memory persistence all amplify the risk.
The question isn’t whether prompt injection is possible, but whether your system makes those failure paths visible and controllable.
Mindgard’s AI Security Risk Discovery & Assessment helps teams understand where prompt injection exposure actually exists. It maps how models are deployed, what data they ingest, which tools they can reach, and where trust boundaries collapse. Instead of guessing, teams see real execution paths, real authority transitions, and real risk.
Mindgard’s Offensive Security solution goes a step further by testing systems the way attackers do. Automated red teaming simulates prompt injection variants, bypass techniques, and agent-level failures that only appear at runtime. This exposes weaknesses before attackers find them and before automation amplifies the damage.
Better prompts, stricter filters, or more safety tuning alone can’t eliminate prompt injection. Request a demo today to learn how Mindgard can help you manage prompt injection as a systemic security risk.
Unfortunately, no. Prompt injection isn't a software bug but a fundamental systemic risk arising from how Large Language Models (LLMs) work. They process all text (system instructions, user input, and retrieved data) as a single stream of tokens without an inherent understanding of trust. Safety training reduces risk but cannot guarantee complete prevention.
Agents chain multiple steps (reading, planning, acting). A single, successful injection early in a workflow can poison memory, alter planning, or trigger unauthorized tool/API calls in subsequent steps, all without human review. The attack impact compounds as trust propagates automatically through the system.
Beyond just generating unwanted text, prompt injection can lead to:
More advanced and capable AI models are often more vulnerable. A model that is better at following complex, nuanced instructions is also better at following complex, malicious ones. Advances in reasoning and autonomy expand the attack surface, creating a fundamental trade-off between capability and security.
