LLM Security: Top 10 Risks and 5 Best Practices
Every new system that reads, writes, or thinks for you is also a new system that can be wrong, be tricked, or be weaponized. Large language models are not just code: they are behaviour. Behaviour that can leak, mislead, or fail in ways your old software never could.
That’s why LLM security isn’t a checkbox, it’s a mindset. It’s the difference between launching with confidence, launching and hoping. When companies hire AI development services or partner with a Gen AI development company, they want creativity, but they also want satisfaction. They want models that delight customers and survive audits.
Below: ten crisp risks and five practical practices. Read them. Then ask: which one would ruin your reputation if it happened next Tuesday? Fix that first.
Top 10 Risks in LLM Security (with OWASP Insights)
Every product leader fears the same thing: the model works… until it doesn’t. So here are the 10 biggest risks to watch, a mix of what we’ve seen in real projects and what global security experts at OWASP highlight for LLMs.
1. Prompt Injection — attackers rewrite the rules
Malicious inputs can trick your model into ignoring guardrails, leaking data, or executing harmful instructions. The answer: validate every input, sanitize prompts, and monitor for anomalies.
2. Insecure Output Handling — the wrong words can cost millions
Outputs that aren’t filtered can expose sensitive data or generate offensive content. Strong output validation, confidentiality policies, and continuous monitoring make sure nothing slips through.
3. Training Data Poisoning — corrupted data, corrupted models
If attackers sneak poisoned samples into training, the model learns the wrong lessons. Source from trusted data, verify constantly, and use anomaly detection to catch tampering.
4. Model Denial of Service — drowning your LLM with queries
Attackers flood your system with endless or complex queries, knocking out real users. Rate limiting, robust authentication, and auto-scaling defenses keep services alive.
5. Supply Chain Weakness — an unseen entry point
LLMs depend on plugins, APIs, and third-party services. One compromised dependency can open the whole system. Audit your vendors, patch often, and run security checks across the stack.
6. Sensitive Information Disclosure — secrets on display
Models can blurt out proprietary code, PII, or internal data. Encrypt, anonymize, and sanitize outputs — and run privacy audits so you know before a regulator does.
7. Insecure Plugin Design — one weak link, total compromise
Plugins expand capability but also expand risk. Without secure coding and input validation, they create easy entry points. Secure design and periodic assessments are non-negotiable.
8. Excessive Agency — acting on its own without control
Give a model unchecked control and it will make choices you never approved. Bound autonomy with clear constraints and keep a human in the loop.
9. Overreliance — blind trust is bad strategy
LLM tools need to be cross verified, check their answers, build fallback processes, and educate users on where the boundaries lie.
10. Model Theft — stealing your competitive edge
An exposed or cloned model means your IP walks out the door. Protect with encryption, watermarking, strict access controls, and monitoring for suspicious activity.
5 Best Practices (Applied, Not Aspirational)
If the risks are the map, these practices are the compass. Do them in order. Start with the one that would hurt you most.
1. Threat Modeling Before You Train
Ask: Who benefits if this fails? What does failure look like? Map the attack surface: inputs, outputs, integrations, data flows.
Practical step: run a 2-hour tabletop with engineers, product, legal, and ops. Produce a list of the top three “if this happens, we lose customers” scenarios and an owner for each. LLM security runs workshops like this and turns the results into a prioritized mitigation backlog.
2. Pipeline Protection for Clean Data
Don’t feed the model raw secrets. Replace PII with tokens. Use synthetic datasets where possible. Log transformations and keep immutable metadata for every training artifact.
We help implement data-contracts and provide tooling so your ML teams can version, test, and reproduce datasets — crucial when you must prove compliance.
3. Runtime Controls — filters, validators, and human-in-the-loop
At runtime, don’t trust the model alone. Validate outputs with retrieval checks, explicit policy filters, and risk scores. For high-stakes responses, require human review before execution.
LLM Security implements layered runtime stacks: a fast filter for profanity and privacy, a verification layer for facts, and a human escalation channel exactly when it’s needed.
4. Continuous Red-Teaming and Monitoring
Security is not a one-off audit. Red teams probe your model like attackers would. Monitoring tells you when performance or behavior deviates from expectation.
We provide scheduled red-team engagements and deploy anomaly detection that flags both subtle and blatant deviations. Alerts link back to artifacts so engineers can trace root cause fast.
5. Productize Safety — integrate into delivery pipelines
Safety checks belong inside CI/CD. Don’t treat safety as an external gate. Automate fairness tests, hallucination regressions, and access policy verification into your model deployment workflow.
LLM Security helps convert manual checks into automated gates so that every deployment ships with safety metadata and roll-back plans.
Core Offerings of LLM Security
If you hired a Gen AI development company to build an assistant or a search experience — here’s how LLM Security plugs in, end-to-end:
- Secure Model Development (Fine-tuning & Instruction Tuning)
its important to fine-tune models with safety-aware objectives, label-efficient methods, and privacy-preserving transformations. Works with your existing ML development services teams or as our managed offering.
- Data Governance & Synthetic Pipelines
Replace risky train data with verified synthetic variants. Version and sign every dataset. Your audit trail becomes a competitive advantage.
- Runtime Protection Stack
Prompt-sanitization, policy filters, provenance-based RAG, and permissioned function calls. The stack can be shipped and tailored to your product.
- Extraction & Watermarking Tech
Prevent model theft with watermarking and query-hardening. It must also run simulated extraction attempts to measure risk.
- Red Teaming & Attack Simulation
Ongoing adversarial testing across prompts, integrations, and UIs. You get findings, prioritized fixes, and evidence to prove due diligence.
- Monitoring, Observability & Drift Detection
Behavior baselines, regression tests, and automated rollback triggers. When behavior drifts, you get an alert with context and next steps.
- Compliance & Evidence Packs
Ready-made reports for privacy, fintech, healthcare or your regulator of choice. This process generates artifacts that matter-data lineage, decision logs, and test histories.
- Consulting & Integration for AI Development Services Teams
Work with your in-house AI development services or plug into vendor stacks. If you’re hiring a Gen AI development company, partner with them to make sure what launches stays safe.
How this changes product decisions (short list)
- Prioritize features that reduce user risk before features that increase engagement.
- Convert “nice to have” telemetry into “must-have” audit trails.
- Treat safety as a product KPI, not a checkbox.
If you’re a product leader, you’ll like this: safer systems scale. If you’re a marketer, you’ll like this: safe products keep customers longer.
A Final Word — build the future you want to keep
There’s a temptation to ship brilliance fast. There’s also a moral and ai business solutions imperative to keep it from burning you down. Security for LLMs isn’t a ledger entry or a PR line. It’s the practical art of making something helpful and ensuring it cannot be abused.
If you’re ready, start with two things today:
- Run one tabletop on what failure would look like in your product.
- Add one automated test into your model CI that would prevent that failure.
LLM Security helps teams do both —acting as a partner to your ML development services and a practical extension of any Gen AI development company you choose. If you want models that scale and keep your reputation intact, they must skip the jargon and get to work.
