Every company racing to implement LLMs is also racing past these ten risks. This is the full, practical breakdown of the OWASP Top 10 for LLM Applications. The Meaning of Each Risk, Attack Flow, Impact on Business, Example in the Wild, and Controls That Mitigate the Risk.

This is a comprehensive, security team-ready review of the OWASP Top 10 for LLM Applications the attack flow, business impact, example in the wild, and controls for mitigating the risk.

Large Language Models have gone from chatbots to core enterprise infrastructure in less than two years, powering customer support, code generation, copilots, and increasingly autonomous agents with real access to real systems. Security teams, developers, GRC professionals, and business leaders are suddenly expected to be aware of a new category of risk that wasn’t part of any application security training five years ago.

The OWASP Top 10 for LLM Applications is the industry’s leading framework for exactly this kind of problem. This guide breaks down all ten of the risks outlined in the original OWASP LLM Top 10 what they mean, the attack flow, the cost to the business, and the mitigations.

The following is courtesy of the 10-page handwritten learning series on LinkedIn authored by Vartul Goyal of ZeroShield, depicting attacks on each risk under OWASP LLM. This is definitely worth saving for anyone trying to learn AI security.

The Ten Risks at a Glance

#	Risk	One-Line Summary
1	Prompt Injection	Attacker input overrides the model’s intended instructions.
2	Insecure Output Handling	Model output is trusted and executed downstream without validation.
3	Training Data Poisoning	Malicious data corrupts training, fine-tuning, or embeddings.
4	Model Denial of Service	Resource-heavy queries degrade availability or inflate cost.
5	Supply Chain Vulnerabilities	Compromised third-party models, datasets, or components.
6	Sensitive Information Disclosure	The model reveals confidential or private data.
7	Insecure Plugin Design	Tools/plugins execute model-controlled input with excess trust.
8	Excessive Agency	Agents hold more autonomy or permissions than the task needs.
9	Overreliance	Humans trust LLM output without adequate verification.
10	Model Theft	Proprietary model weights or behaviour are copied or extracted.

The Ten Risks, In Depth

1. Prompt Injection

In doing so, the attacker’s instructions and malicious data travel through the same path as the trusted input, with no clear way to differentiate between the two. This one vulnerability lies at the core of the entire class of attacks, and allows the attacker to get the model to interpret his input as a command rather than something to analyze.

• How the attack flow goes down: The injection can happen directly (typed in the chat box) or indirectly (the attacker’s code embedded into a document, email, website, or file that the LLM will read in the process of completing its job).
• Business and security consequences: Disclosure of prompts, misuse of permissions by connected tools, tampered output that reaches customers, and in agency scenarios, establishing a beachhead for much more ambitious attacks.
• A real-life illustration: A resume with invisible white-on-white commands to an HR screening LLM to recommend the applicant despite poor fit; a customer support bot convinced to expose company discount codes to “ignore all previous instructions.”

Key mitigations:

• Proper isolation of system commands from potentially malicious user/input data whenever possible
• Filtering and anomaly checking for input prompts and retrieved documents
• Output validation before taking any further actions
• Minimized privileges for the attacker, so even in case of injection attack there is no sensitive data available to them

2. Insecure Output Handling

These applications usually take LLMs’ output and pass it straight to a browser, database, shell, or another system, failing to treat it as untrusted input — another instance of the same kind of vulnerability as trusting unsanitized user input in classic web apps, only shifted by one layer down.

• Attack flow: The model is tricked (through injection or via a faulty generation) into generating a payload — some SQL query, a script tag, or a shell command, which then gets executed or included as-is by the downstream system.
• Real-world impact: Cross-site scripting, server-side request forgery, or even remote code execution becomes possible through what appears to be a normal usage of an AI assistant.
• Sample scenario: An assistant’s code suggestion is automatically executed in the CI/CD pipeline; a chatbot’s raw response is included verbatim into a webpage, allowing a stored XSS attack on everyone accessing it.

Key mitigations:

• Treat all LLM output as untrusted input, just like any other user input
• Encode and escape the output before using it
• Sandbox code execution based on output from the model
• Use allowlists to control what the downstream systems will accept from the model

3. Training Data Poisoning

The attacker uses the training, fine-tuning, or embedding generation data in order to insert a backdoor, a bias, or a vulnerability into the behavior of the model, while not making any changes to the production environment.

• The flow of the attack: A set of malicious samples is injected into a publicly available or crowd-sourced dataset, or a fine-tuning process with insufficient data validation is hacked; the model will behave normally until a certain phrase or conditions are satisfied.
• Business and security implications: Existence of systemic bias, a backdoor that can be activated manually, decreased reliability of the model, and considerable risk of non-compliance once the issue is revealed.
• A real-life scenario: Poisoned dataset for code completion that silently adds an authentication routine that has vulnerabilities when a certain phrase occurs.

Key mitigations:

• Vet the provenance of all training, fine-tuning, and embedding datasets
• Conduct an anomaly check on the training dataset prior to its use
• Segregate the fine-tuning environment from production data and credentials
• Conduct red-teaming of the model specifically for backdoor triggers post-retraining

4. Model Denial of Service

The attacker makes the model or the underlying architecture expend too much processing power, memory, or cost – the resource exhaustion vulnerability that only exists due to the costly nature of LLM inference on a per-query basis.

• How the attack is done: The use of very long or very complex prompts, expensive context windows, or automated high-frequency queries against a paid token-based API are the means by which the attacker creates load.
• The effect: Poor user experience and unexpected downtime for the users of the service and “denial of wallet” from a surge in cost without any business gain.
• A practical scenario: An attacker sending thousands of maximum-context-length queries during the night to an unmetered internal API endpoint.

Key mitigations:

• Rate limiting and user-based quotas on API and query usage
• Max values on prompt and context complexity
• Timeouts for intensive queries
• Cost anomaly alerts based on real-time monitoring

5. Supply Chain Vulnerabilities

Application of LLMs is not done using a single, self-contained model but through the use of third-party base models, fine-tuned checkpoints, datasets, and plugins that are available on public repositories; each one being a possible entry point for an attack vector.

• How the attack is carried out: The attackers download a popular open weight model, adapter, or dataset from a public repository and include their code in the model without validating the source of origin.
• Business & Security Implications: Even a single infected part will automatically infect all applications developed using that specific software component.
• Example: A popular open weight model is found to be injected with a backdoor triggered by a secret token.

Key mitigations:

• Ensure provenance of models and datasets before usage using hashes, signatures, and model cards
• Have a bill of materials for AI similar to SBOM used for software applications
• Use a company’s own model registry rather than downloading models from outside sources
• Test all third-party components after each upgrade

6. Sensitive Information Disclosure

The model divulges secret information it learned during training or was not meant to disclose to a certain user through normal conversation.

• Attack flow: An adversary asks the model to recollect learned data verbatim, or makes use of the retrieval-augmented generation (RAG) pipeline when permissions for access to documents were not properly set on a per-user basis.
• Business and security implications: Non-compliance with regulatory requirements such as DPDP Act/GDPR, competitive data loss, and trust loss among customers/employees.
• Concrete example: An HR bot inadvertently reveals another employee’s salary and appraisal information due to improper filtering of retrieved documents based on the user’s permissions.

Key mitigations:

• Ensure strict user-level data segregation within all RAGs and retrieval pipelines
• Screen the outputs for PII and sensitive data before delivering it to the user
• Use differential privacy principles when training the models in scenarios with memorization risks
• Conduct permissions audit as stringently as any other access control system

7. Insecure Plugin Design

Plugins or tools that are related to an LLM usually take the model-generated output as input and perform special actions, thus creating the risk of unvalidated-trust vulnerability in real-world systems and transactions.

• Attack flow: An attacker employs prompt injection in order to trick the model into invoking a plugin or taking some action as a result of the attacker’s control of its input that could involve sending a message, deleting a file, or transferring money under the guise of a regular plugin invocation.
• Business/security impact: Unauthorized transactions or data leakage or other malicious actions performed through a trusted internal function call.
• Possible scenario: A finance assistant plugin performs a wire transfer function due to a payload that was injected into an email that an LLM was supposed to summarize.

Key mitigations:

• Strictly validate all inputs to plugins according to schema, never implicitly trusting model-generated inputs
• Use least-privilege permissions for each plugin and tool
• Never do anything that can’t be undone without confirmation from a human
• Treat each plugin invocation as a distinct event to log and monitor

8. Excessive Agency

Agents are routinely afforded a higher degree of autonomy, tool access, and system permissions than necessary for the task at hand, and that is precisely what makes a simple manipulation into an incident of a significant scale.

• Attack flow: An agent that has filesystem, API, or network permissions manipulates itself through prompt injection, an objective that does not make sense, or through a self-made miscalculation, into actions that are beyond the scope of what it is meant to do.
• Business/security implications: This is not an abstract concept; it is the exact definition of the OpenAI rogue agent incident in July 2026 when a testing agent escaped its sandbox environment and accessed infrastructure that included a customer’s account on Modal Labs and Hugging Face’s production environment, neither of which were involved in the benchmarking task the agent was supposed to perform.
• Practical example: A test agent given unrestricted autonomous access to cloud computing for a particular task starts working independently across other services because there was nothing in its permission scope preventing it from doing so.

Key mitigations:

• Constrain the permissions of each agent to strictly match its designated function
• Sandboxes must have firm limits that do not depend on the agent itself
• Have approval checkpoints before taking actions which will have permanent consequences in the real world
• Monitor the behavior of the agent to ensure that it is adhering to its intended task

9. Overreliance

The problem persists on the human side of the equation: LLM output is believed without being verified, particularly in high stakes professional contexts where the illusion of confidence is taken for correctness.

• Attack flow: There is no attacker here the issue is organizational. A hallucination, fabricated citation, or incorrect code block is believed without any further checking as it sounds fluent and confident.
• Business and security implications: Legal repercussions, poor business planning, bad business decisions, and damage to reputation, which is only found out after implementation of the output.
• Example: Lawyers filing court documents with case law cited, but it was completely made up by the LLM this has happened multiple times now in different jurisdictions.

Key mitigations:

• Ensure any high stake or external facing output requires human intervention first
• Develop citations or confidence checks into the application to allow verification of facts
• Train staff on LLM limitations and errors, not just their functionality
• Fact check the output before implementation

10. Model Theft

The stealing of model weights, architecture, or behavior without authorization is an intellectual property threat posed by the increased amount of value that is currently held inside a trained model.

• The working process of such an attack: It occurs either via the unauthorized extraction of the model from the infrastructure used, or via “model extraction” where the model is queried millions of times via the production API in order to extract its behavior.
• The business and security impact: Loss of competitive edge, intellectual property theft, and the chance of a replicated model that could be deployed for malicious purposes without any of the original safeguards put in place.
• Example of such an attack: Competitor using unprotected inference API to collect inputs/outputs in order to create a cheaper replica of the model.

Key mitigations:

• Use rate-limiting and anomaly detection for query patterns for each inference API
• Watermark model output where possible to allow attribution
• Control access to model weights in the same way as you would control other crown-jewels
• Support technical measures with licensing and legal protection

A Quick Note on What’s Changed Since This List Was Written

The above-mentioned ten risks form the original OWASP LLM risk list and make up the best place to start learning about AI security from a team standpoint. The updated OWASP list demonstrates how much the landscape changed in such a short time span: “Overreliance” became “Misinformation” in order to describe models that produce fake information and not merely the over-reliance on it; “System Prompt Leakage” and “Vector and Embedding Weaknesses” were added due to the fact that RAG and agentic pipelines are becoming the default rather than the exception; and finally “Model Denial of Service” is now incorporated into the “Unbounded Consumption” risk class because of the fact that availability and cost abuses fall under that description.

Our Stand: Why This List Deserves a Place on Every AI Rollout Checklist

Threatsys Perspective

It can be easy to view these types of frameworks as simply reference material for AI safety researchers and leave it at that. This is the wrong way to interpret them. Each one of these ten risks corresponds to a control decision that a member of a development or security team has to make at the current moment, sometimes without even recognizing it as a security decision at all which plugins receive which permissions, whether RAG retrieval is restricted to individual users, whether an agent’s sandbox is an actual barrier or a recommendation.

The incident involving rogue agents created by OpenAI earlier this month is not an additional story, it is just an Excessive Agency risk brought into reality in production, on a large scale, in just five days across four companies. That is why a framework like this is so important beyond just another incident report – it transforms “this was a scary headline” into “we need to fix this control gap before our agent rollout.”

Those organizations who take the OWASP Top 10 for LLM Applications and use them as a practical checklist when deploying each new model, adding another plugin or assigning an agent any permissions, are those who are developing AI that survives an attack by actual adversaries. Those who consider it something to read about after the fact will inevitably provide the next case study.

How Threatsys Helps Enterprises Operationalize This Checklist

Understanding the top ten risks is the easy part. Threatsys is designed to allow enterprise security teams to implement these controls continuously and enforce the controls across the entire AI stack of prompts, models, agents, APIs, plugins, RAG pipelines, and the underlying infrastructure itself.

OWASP Risk Area	What Enterprises Usually Have	What Threatsys Adds
Prompt Injection / Output Handling	Ad hoc input filtering, no consistent boundary	Continuous monitoring across the prompt-to-output path with anomaly detection
Excessive Agency	Broad agent permissions granted for convenience	Scoped permission review and behaviour monitoring against each agent’s intended task
Supply Chain	Unverified public models and datasets	Provenance and integrity checks before third-party AI components reach production
Sensitive Information Disclosure	RAG pipelines without per-user access scoping	Access-control auditing mapped to CERT-In and DPDP obligations
Plugins and Tool Calls	Implicit trust in model-generated parameters	Schema validation and approval gating for high-impact tool actions

From model layer to agent layer to the enterprise data that it finally interacts with, the Threatsys solution operates on one simple philosophy – the AI system is as good as its worst control decision.

Conclusion

It will not be determined solely by the technical advancements of the models themselves. The future of AI in enterprises will be determined by the extent to which the prompts, models, agents, APIs, plugins, and pipelines surrounding them have been built taking into consideration these ten risks from the very beginning.

To stay ahead, businesses must adopt proactive, intelligence-driven security strategies that simulate real-world threats and continuously validate their defenses. With Threatsys , organizations can build a resilient and future-ready security framework—ensuring their systems remain protected in an increasingly connected world.