LLM Security Risks: What Every CTO Must Know Before Shipping AI Features

According to IBM’s 2025 Cost of a Data Breach Report, 63% of breached organizations still lacked AI governance policies. Yet many teams continue to ship LLM applications like ordinary microservices. That approach fails.

LLM security risks are LLM vulnerabilities unique to large language model applications. They include prompt injection, data leakage, training data poisoning, insecure tool use, and supply chain attacks. Unlike traditional application security, LLM security risks span four layers at once: the model, the prompt, the data, and the agent loop. This guide is for CTOs who make build-time and ship-time decisions. If you need help shipping LLM features safely from day one, see our AI development services.

Key Takeaways

• LLM security risks span four layers: the model, the prompt, the data, and the agent loop. Traditional AppSec covers none of them directly.
• The OWASP Top 10 for Large Language Model Applications is the canonical risk list every engineering team should map their features against.
• Agentic AI introduces six risks not in the original OWASP framing, including tool poisoning, memory contamination, and confused-deputy attacks across MCP servers.
• Mitigation is an engineering pattern, not a vendor tool. Input validation, output filtering, runtime guardrails, and least-privilege permissions must ship with the feature, not bolt on after.
• The pre-deployment checklist near the end of this article gives CTOs a 10-step gate against all major LLM security threats before any LLM application reaches production.

What Are LLM Security Risks?

LLM security risks are LLM vulnerabilities unique to applications powered by large language models. Large language model security differs from traditional web application security in three structural ways:

• Non-deterministic behavior. The same input can produce different outputs. Security outcomes vary across runs.
• Natural-language instructions. Anyone who influences the prompt can issue commands.
• Third-party supply chain. The underlying LLM is often a black-box system hosted by a provider you do not control.

As a result, the attack surface spans four distinct layers:

• Model layer: the weights, training data, and provider supply chain.
• Prompt layer: system prompts, user inputs, and any text injected through retrieval or tool outputs.
• Data layer: what user data the system can see, what it stores in context, and how that data is logged or cached.
• Agent layer: tool use, function calling, and multi-step reasoning loops where the LLM takes actions on systems beyond text generation.

Traditional firewalls, WAFs, and SAST tools cannot see most of these layers. Defending against LLM security risks requires layer-specific security controls, not a perimeter upgrade.

What Makes LLM Security Risks Different from Traditional AppSec?

The OWASP Top 10 LLM Security Risks (2025 Update)

The OWASP Top 10 for Large Language Model Applications is the most widely adopted taxonomy for LLM vulnerabilities and large language model security risks. The 2025 update reflects how the threat model has shifted toward agent-driven systems and supply chain attacks. Below is the working list of LLM security risks every CTO should know before approving a launch.

Not all risks weigh equally. The matrix below summarizes the OWASP Top 10 LLM security risks for 2026 with likelihood and impact ratings, ordered by OWASP ID:

*Hallucination impact rises to High or Critical in medical, legal, and financial contexts where the business absorbs liability for wrong answers.

Each of these LLM security threats is detailed below with definition, business impact, and the matching mitigation pattern. The 10 LLM vulnerabilities together represent the full attack surface for any 2026 LLM deployment.

LLM01: Prompt Injection

• Definition: Prompt injection is an attack where adversarial input overrides the system’s intended instructions. Direct injection comes via user input. Indirect injection hides in documents, web pages, or API responses the LLM reads.
• Why it matters: Prompt injection is the most exploited LLM security risk in 2026. Language itself is the attack vector, so filtering is hard. A successful attack can steal data, trigger unauthorized tool calls, or hijack the agent.
• Mitigate it: Separate system instructions from user content with delimiters, apply input validation and prompt filtering, and never let a single injection escalate to a tool action without authorization, see Engineering Mitigations → Input Layer.

LLM02: Sensitive Information Disclosure

• Definition: Sensitive information disclosure, or data leakage, happens when an LLM leaks confidential data through its output. The exposed data can include PII, source code, system prompts, or proprietary business data. The leak comes from memorized training data or session context.
• Why it matters: A single data leakage incident can trigger GDPR fines up to 4% of revenue, EU AI Act fines up to 7%, and lasting brand damage. Samsung banned ChatGPT after engineers leaked source code. Treat the LLM as an exfiltration channel.
•  Mitigate it: Apply output filtering for PII and secrets, redact sensitive fields by user role, and enforce access control on everything the model can read, see Data Layer.

LLM03: Supply Chain Vulnerabilities

• Definition: LLM supply chain vulnerabilities are LLM security risks from compromised LLMs, poisoned datasets, malicious plugins, or tampered tokenizers. The risk applies to any third-party component the app loads.
• Why it matters: Most enterprise LLM applications depend on an external LLM provider, open-source libraries, and an embedding pipeline. One compromised dependency reaches every app built on it. Researchers identified roughly 100 suspicious models on Hugging Face in 2024, including several that contained malicious code.
• Mitigate it: Pin and hash model versions, verify provenance on any third-party model or weights, and review the provider’s security posture before adoption, see Model Layer.

LLM04: Data and Model Poisoning

• Definition: Data and model poisoning is an attack where adversaries contaminate training data, fine-tuning datasets, or retrieval corpora to plant hidden behaviors in the model. The most dangerous variant is a backdoor that stays dormant until a trigger phrase activates it. A related class, adversarial (evasion) attacks, crafts inputs that look benign but reliably push the model into wrong or harmful outputs at inference time.
• Why it matters: A poisoned LLM behaves normally on test data and only fails when an attacker triggers it. Detection after deployment is hard. The risk grows with fine-tuning and open-source adoption. Security assessments must cover every training dataset.
• Mitigate it: Source training data from verified origins, run anomaly detection during training, and validate every retrieved chunk before it enters context, see Data Layer.

LLM05: Improper Output Handling

• Definition: Improper output handling is one of the most common LLM vulnerabilities. An app treats LLM output as trusted text and passes it to a browser, shell, database, or API. The LLM then becomes a vector for XSS, SSRF, or remote code execution.
• Why it matters: This is one of the most common engineering mistakes in LLM applications. The fix: treat LLM output like user input. The cost of ignoring it ranges from defacement to full system compromise.
• Mitigate it: Enforce structured output (JSON schema / function calling), sanitize responses, and never pass raw model output to a downstream interpreter, see Model Layer.

LLM06: Excessive Agency

• Definition: Excessive agency is when AI agents hold more permissions than the task requires. It comes from over-broad plugin scopes, weak function-calling permissions, or default-allow configs in AI applications.
• Why it matters: Overprivileged AI agents turn a small prompt manipulation into a major incident. With MCP and agentic AI adoption rising in 2026, this large language model security risk is climbing fast. Excessive agency causes the most irreversible damage: sent emails, deleted records, or transferred funds.
• Mitigate it: Apply least-privilege permissions per tool, require human-in-the-loop for irreversible actions, and authorize per call, not per session, see Agent Layer.

LLM07: System Prompt Leakage

• Definition: System prompt leakage exposes internal instructions, business logic, or secrets in the system prompt. Extraction techniques are well-documented and work on production systems.
• Why it matters: Leaked prompts reveal how the app reasons, letting attackers craft better injections. If engineers placed credentials in the prompt, leakage becomes a data breach. This is one of the easiest-to-exploit LLM vulnerabilities. Assume the prompt will leak.
• Mitigate it: Keep credentials and business secrets out of the prompt entirely, and confirm during red teaming that the model cannot reveal its system prompt, see Input Layer.

LLM08: Vector and Embedding Weaknesses

• Definition: Vector and embedding weaknesses attack the retrieval pipeline of a RAG system. Adversaries poison embeddings, inject malicious documents, or craft queries that pull harmful content into the context window.
• Why it matters: RAG is now the default architecture for enterprise LLM applications, making the vector store a high-value target. A single poisoned chunk affects every query that retrieves it. Detection requires monitoring retrieval patterns.
• Mitigate it: Enforce access control on the vector store, validate and bound retrieved chunks, and treat retrieved text as untrusted, see Data Layer.

LLM09: Misinformation and Overreliance

• Definition: Misinformation and overreliance is the risk that users trust hallucinated LLM output and act on it. Hallucination is the behavior. Overreliance is what turns it into a business problem.
• Why it matters: In healthcare, legal, and finance, the business absorbs liability for wrong answers. The Mata v. Avianca case is now taught by bar associations, after a lawyer cited LLM-fabricated court cases as real precedent. UI confidence signals are not optional.
• Mitigate it: Add UI confidence signals and source citations, keep a human in the loop for high-stakes outputs, and evaluate models before promotion, see Model Layer.

LLM10: Unbounded Consumption (and Model Theft)

• Definition: Unbounded consumption is the LLM denial-of-service category. It covers excessive request volume, token exhaustion, and model theft / extraction, where adversaries query the LLM to clone its behavior or reconstruct proprietary configuration.
• Why it matters: This is the easiest LLM attack to launch and weaponize against billing. A small attacker can drive five-figure cloud bills overnight. Detection works only if observability covers token usage, not just request count.
• Mitigate it: Set per-tenant token budgets, rate limits, and cost ceilings, and alert on token-usage anomalies, see Observability Layer.

Agentic AI Risks the OWASP Top 10 Does Not Fully Cover

OWASP framing covers single-shot LLM applications well. It is incomplete for agentic AI systems shipped in 2026. The following LLM security risks for AI agents need dedicated security controls.

These six LLM agent security risks share one pattern. The attack surface is no longer a single request and response. Instead, it is a graph of LLM calls, tool calls, and memory reads that evolves over the agent’s lifetime. Defenses must operate across that graph, not at one endpoint.

Real-World LLM Security Incidents (2023-2025)

Public incidents make these LLM security risks concrete:

1. Bing Chat Indirect Prompt Injection (2023)

Researchers showed that a malicious webpage could manipulate Bing Chat through indirect prompt injection. By embedding hidden instructions in web content, attackers influenced the LLM’s behavior and triggered data exfiltration or phishing scenarios.

Why it matters: Any external content an LLM reads, including webpages, emails, documents, or API responses, can become an attack vector.

Sources: Greshake et al., Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection, arXiv (2023).

2. Samsung Internal Data Leak Through ChatGPT (2023)

Samsung engineers pasted proprietary source code and confidential meeting notes into ChatGPT while debugging. Within weeks, Samsung restricted generative AI on company devices.

Why it matters: Employees can unintentionally turn public AI services into data exfiltration channels. The incident highlighted the need for enterprise AI governance and data loss prevention controls.

Sources: Tom’s Hardware, Samsung Fab Workers Leak Confidential Data While Using ChatGPT (April 2023).

3. Microsoft 365 Copilot EchoLeak (CVE-2025-32711)

Security researchers discovered EchoLeak, the first publicly disclosed zero-click prompt injection vulnerability in a production LLM application. A single crafted email could cause Microsoft 365 Copilot to exfiltrate enterprise data without any user interaction.

Why it matters: EchoLeak proved that prompt injection is no longer theoretical. It is a practical, high-severity vulnerability class affecting enterprise AI systems.

Sources: EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System, arXiv (2025).

Engineering Mitigations That Must Ship with the Feature

Mitigation is layered. No single security control covers every LLM vulnerability. The five layers below are the minimum for a 2026 production deployment of LLM applications.

Input Layer

Apply these security controls before user text reaches the LLM:

• Use prompt filtering and input validation on all inputs.
• Strip or escape control sequences.
• Separate trusted system instructions from untrusted user content with clear delimiters.
• Harden the system prompt against override attempts with vigilant prompting.

Model Layer

Harden the LLM with these practices:

• Pin the model version and log the model hash in production telemetry.
• Enforce structured outputs through JSON schema or function calling.
• Strip secrets, PII, and shell-like content with output filtering before any response leaves the system.
• For high-stakes applications, evaluate models with lm-evaluation-harness or red teaming before promotion.

For a complementary view focused on AI-assisted coding rather than runtime LLM apps, see our vibe coding security guide.

Data Layer

Protect what the LLM can see, store, and log:

• Apply data localization so PII never leaves its allowed region.
• Anonymize inputs that do not need to be identifiable.
• Strip sensitive fields based on user role.
• Validate every retrieved chunk and enforce access control on the vector store.
• Log who can see what, then audit the logs.

Agent Layer

Treat the agent as a deputy whose authority is checked at every call:

• Apply least-privilege permissions to every tool.
• Force human-in-the-loop for irreversible actions like sending money or deleting data.
• Use containerization or sandboxing for tool execution.
• Validate tool outputs before they re-enter the LLM context.

Observability Layer

Make every LLM action visible to your security stack:

• Wire every prompt, response, and tool call into your SIEM.
• Apply anomaly detection on token usage and tool patterns.
• Build runtime guardrails that can break a session in flight.
• Adopt AI security posture management (AI-SPM) to unify these signals. Open-source options like Granite-Guardian and TrustyAI work alongside commercial AI-SPM platforms.

The pattern below summarizes how these layers stack:

Pre-Deployment LLM Security Checklist for CTOs

This 10-step gate is what Saigon Technology runs before any LLM application ships. It addresses every major risk in the OWASP Top 10. It is structured so engineering leads can adopt it as a launch criterion.

1. Threat-model the feature. Map each component to the OWASP Top 10 for Large Language Model Applications. Identify which LLM security risks apply, and which are out of scope with justification.
2. Pin the model version. Log model hash, provider, and pinned version in production. Document the rollback plan for any model upgrade.
3. Enforce prompt isolation. Separate system instructions from user input. Confirm during red teaming that the LLM cannot reveal the system prompt.
4. Implement structured output. For any downstream code that consumes LLM output, enforce a JSON schema or function-calling contract. Reject outputs that fail the schema.
5. Apply guardrails and output filtering. Deploy runtime guardrails that catch PII, secrets, and prompt-injection signatures before the response leaves the system.
6. Set token, rate, and cost guardrails. Per-tenant token budgets, rate limits, and cost ceilings shut down unbounded consumption and model denial of service.
7. Red team the prompt surface. Run adversarial prompts and jailbreaking patterns. Use both automated red teaming tools and human reviewers.
8. Verify third-party provider posture. Review the LLM provider’s SOC 2 report, ISO 27001 status, data processing agreement, and data retention policy.
9. Wire observability into SIEM. Log every prompt, response, and tool call with user context, LLM version, and trace IDs. Anomaly detection runs on this stream, not web logs.
10. Define incident response plans and a kill switch. Document who owns LLM-incident response, how to disable the feature, and how to notify users. Run a tabletop exercise before launch.

Any feature failing these ten checks is not ready to ship.

How Saigon Technology Builds AI Features with Security by Design

The mitigation patterns and pre-deployment checklist above are not theoretical. They reflect how Saigon Technology builds LLM applications for enterprise AI clients. Security work for AI applications happens during build, not post-launch.

Saigon Technology’s standard practice for addressing LLM security risks across enterprise AI engagements includes:

• A pre-deployment review against the 10-step checklist above.
• AI engineers embedded in client teams under a forward-deployed engineering model, so controls like prompt isolation, output filtering, and tool authorization are coded in, not bolted on.
• Default architecture: Zero Trust patterns, AES-256 encryption at rest, and OAuth 2.1 for identity.
• ISO 27001 and ISO 9001 certifications, plus Microsoft Gold Partner status.

Two examples from production:

• Wealth Management Platform (US fintech). GDPR-compliant LLM-assisted features built on Azure microservices, with audit trails on every prompt and tool call. The system has been live for over two years.
• AxiaGram (healthcare). A HIPAA workflow platform handling more than 6 million records under management.

These engagements run through Saigon Technology’s generative AI integration services and AI development services teams. The company has delivered 850+ projects across 350+ global clients with 400+ engineers, and was ranked #2 Top AI Application Development Platforms in 2025.

FAQs

1. What is LLM security?

LLM security is the practice of identifying and mitigating risks in large language model applications. Large language model security covers four layers: model, prompt, data, and agent loop. Attack categories include prompt injection, data leakage, supply chain vulnerabilities, and excessive agency. These LLM security risks are codified in the OWASP Top 10 for Large Language Model Applications. It differs from AppSec because the LLM is non-deterministic and usually a third-party black box.

2. Why is LLM security different from traditional application security?

Traditional applications execute deterministic code, while LLMs interpret natural language and interact with external data and tools. This creates entirely new attack surfaces that conventional application security controls were not designed to handle.

3. What is the biggest LLM security risk?

Prompt injection is the most exploited LLM security risk and the most common LLM vulnerability. Indirect prompt injection through retrieved documents or tool outputs is the hardest variant to mitigate. Excessive agency in AI agents is a close second in 2026, driven by tool use and MCP adoption.

4. How is prompt injection different from SQL injection?

SQL injection exploits a parser that treats data as code in a deterministic system. Prompt injection exploits an LLM that treats all text as instructions, in a non-deterministic system. SQL injection has a clear fix: parameterized queries. Prompt injection has no equivalent universal fix today. Instead, defenders rely on layered security controls: prompt filtering, structured output, sandboxing, and human-in-the-loop for high-impact actions.

5. Do I need a dedicated LLM security tool if I already have an AppSec stack?

Yes, for any non-trivial production LLM application. Traditional AppSec tools cannot see prompt content, LLM output, tool calls, or vector store activity in LLM applications, so they miss most LLM vulnerabilities. At minimum, add runtime guardrails on inputs and outputs, plus observability into the prompt and LLM supply chain. AI security posture management (AI-SPM) platforms cover several of these in one product.

6. How does GDPR or the EU AI Act change LLM security?

Both add accountability, transparency, and explainability on top of technical security. GDPR requires data minimization, purpose limitation, and a lawful basis for any PII the LLM processes. The EU AI Act classifies many LLM applications as limited or high-risk AI systems, requiring documented risk management, human oversight, and post-market monitoring. Violations carry fines up to 7% of global revenue under the EU AI Act.

Closing Thoughts

LLM security should never come at the expense of innovation. The right approach is to build AI that is secure by design, fast to launch, and cost-efficient to operate.

At Saigon Technology, we can deliver an initial prototype within 24 hours and help organizations move from idea to production while maintaining strong security controls and an accelerated time to market.

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