Most LLM Security Failures Aren't AI Problems. They're Process Problems.
By Victor Akinode
Over the past few years, large language models have quietly moved from research demos into the core of real systems. They now answer customer emails, summarize internal documents, assist developers, and support operational decisions inside organizations that were never designed to host probabilistic, language-driven components.
From my experience working across cybersecurity, applied AI, and model safety, the most common mistake teams make is assuming these systems behave like traditional software.
They do not.
The Fundamental Mismatch
The challenge is not that LLMs are insecure by default, but that they operate under assumptions that do not align cleanly with the systems around them. Traditional application security depends on predictable inputs and constrained behavior. LLMs, by contrast, infer intent, draw connections across context, and generate responses through language rather than rules.
The Capability-Accountability Gap
In several deployments I have reviewed, many models behaved exactly as designed, yet still introduced risk because no one had clearly defined where their responsibility ended and where the surrounding system was expected to take over. This gap between capability and accountability is explicitly called out in the NIST AI Risk Management Framework.
Prompt Injection: The Misunderstood Threat
Prompt injection is one of the earliest and most widely misunderstood symptoms of this gap. In simple terms, it occurs when an attacker uses carefully crafted language to influence a model into ignoring, reinterpreting, or subtly bending its original instructions. The term was coined by Simon Willison in 2022.
What Prompt Injection Actually Looks Like
These prompts rarely resemble the dramatic jailbreak examples many teams rely on during evaluations. Instead, they tend to blend into normal interaction, appearing as reasonable follow-up questions, clarifications, or contextually appropriate requests.
I have seen internal assistants persuaded to reveal system prompts, reframe restricted requests as acceptable tasks, or prioritize user input over security guidance through language that appeared calm, plausible, and well-intentioned. The model did not malfunction; it simply followed the instruction that seemed most compelling within the context it was given.
Data Leakage: The Quiet Risk
Closely related to prompt injection is data leakage, which tends to surface in quieter and less dramatic ways. In many deployments, sensitive context is inserted into prompts as part of normal system operation. In production environments, models have been observed reintroducing this private context into later responses after seemingly benign follow-up questions.
Language models do not natively enforce confidentiality. These boundaries must be imposed at the system and architecture level.
Tool-Augmented Agents: Indirect Action Risk
Risk escalates further when models are allowed to interact directly with tools. Many deployments permit LLMs to query databases, trigger workflows, execute scripts, or call internal APIs. From a security perspective, this introduces a form of indirect action risk that traditional models did not pose.
The Influence Problem
I have encountered systems where a model was technically barred from performing sensitive operations, yet still able to generate requests that downstream services executed without sufficient scrutiny. The model did not need autonomy; it only needed influence.
The Ownership Gap
This leads to a broader, and often uncomfortable, insight: many LLM security incidents appear less like AI failures and more like process failures. In many cases, the model did what it was capable of doing, but the surrounding system lacked clear decision rights, enforcement points, or escalation paths.
Security Team — Product owns behavior. Product Team — Vendor handles safety. Vendor — Customer adds controls. These gaps become clear only after an incident. Nobody.
When responsibility is fragmented or implicit, architecture becomes the only reliable enforcement mechanism.
Architecture-First Security
More resilient deployments therefore start with architecture rather than prompts. In the most robust systems I have worked with, the model is treated as an untrusted component whose outputs are always mediated by deterministic controls.
Architecture-First LLM Security
User Input Layer (Separated from system instructions) → Data Scoping & Filtering (Sensitive data filtered before model) → LLM (Untrusted Component) (Outputs always mediated) → Deterministic Validation Layer (All proposed actions pass explicit checks)
The Multilingual Blind Spot
Multilingual deployment introduces another layer of complexity that remains underappreciated. Many safety evaluations focus almost entirely on English, yet models are increasingly deployed across global user bases.
Language-Based Safety Degradation
In practice, I have seen models behave more permissively when prompted in under-represented languages, not because safeguards are absent, but because they are thinner, less tested, or poorly calibrated. Research like SEA-SafeguardBench demonstrates that safety performance can degrade significantly when models are evaluated using culturally grounded prompts in Southeast Asian languages. For globally deployed systems, multilingual safety should not be an edge case but a core risk factor.
Principles for Secure Deployment
- Treat LLM Interactions As Untrusted Input
Even when they originate from internal users or familiar systems. Natural language is a uniquely powerful attack surface because it can be shaped intentionally or accidentally by anyone.
- Deploy Layered Controls
Deployments must rely on layered controls that reinforce one another so that no single failure leads to systemic impact.
- Test How Systems Actually Fail
Through subtle prompts, embedded instructions in documents, multilingual inputs, and edge cases that do not appear malicious at first glance.
- Assign Clear Ownership
LLM security is not solely a technical challenge but an ownership problem. Responsibility must be explicitly defined when a model produces harmful output, triggers an unsafe action, or exposes sensitive information.
Security as a Discipline, Not a Destination
One lesson remains consistent across research and real-world deployments: security is not a problem to solve once and move past. Models will improve, new failure modes will emerge, and usage will continue to evolve.
Treating LLM security as its own discipline, grounded in continuous learning, is what makes safe deployment at scale possible.
References
- NIST AI Risk Management Framework (AI RMF)
- Simon Willison, Prompt Injection Attacks (2022)
- Microsoft, Securing Generative AI and Enterprise AI Workloads
- OWASP Top 10 for Large Language Model Applications (2025)
- SEA-SafeguardBench, Evaluating AI Safety in SEA Languages
About The Author
Victor Akinode is a Cybersecurity and AI Safety & Security Professional with close to a decade of experience building and securing production systems and advising organizations on real-world AI deployment risks. He is a Master’s Student at McGill University and an AI Safety Researcher at Mila (Quebec AI Institute), where his work focuses on large language model security, AI safety, and governance across enterprise and multilingual contexts.
