How to Break Your AI Model Before Attackers Do

Your AI models are vulnerable right now. While you're reading this, attackers are probing them for weaknesses, looking for ways to poison data, manipulate outputs, or steal sensitive information. And most organizations aren't ready.

You've spent months building sophisticated AI systems. You've trained them on carefully curated data. You've fine-tuned them for performance. But have you actually tried to break them?

Attackers don’t wait for you to get around to testing. They exploit the assumptions in your data pipelines, prompt handling, and model logic. And you’re giving them the advantage if you’re not deliberately testing your AI.

Table of Contents

1. See where your AI is exposed
2. Test like an attacker, Validate like an engineer
3. Techniques for breaking AI models safely
4. Building an AI Security Testing Workflow
5. Measuring Outcomes and Maturity
6. Next Steps for Security Leaders

See where your AI is exposed

AI adds new entry points attackers can exploit: data pipelines, model weights, prompts, and integrations. Most teams don’t map these attack surfaces before launch, so issues show up in production, where they are very expensive to fix. Here’s where AI fails in the real world:

Data poisoning in training pipelines

If an attacker slips tainted records into your training or fine-tuning data, the model learns the wrong behavior. In practice, this looks like toxic or biased outputs, backdoors that trigger on specific tokens, or classification models that fail on attacker-chosen inputs. Poisoning often targets weak intake controls, such as open datasets, partner feeds, user-generated content, or poorly governed labeling work.

What to test

• Can you detect and quarantine anomalous data before it reaches training?
• Do you retrain on clean baselines and compare drift to catch backdoors?
• Can you reproduce a model from signed inputs and artifacts to prove integrity?

Prompt injection and jailbreaks in LLMs

LLMs follow instructions, even malicious ones hidden in inputs, metadata, or linked content. Attackers use prompt injection to override system policies, exfiltrate secrets, or cause actions through connected tools. On the other hand, jailbreaks lower safety guardrails and unlock unintended capabilities. These attacks succeed when models are over-trusted or when retrieval and tool use lack isolation and output checks.

What to test

• Can a crafted input override your system prompt or tool-use policy?
• Do RAG pipelines sanitize retrieved content and constrain what the model can execute?
• Are secrets, connectors, and plugins scoped with least privilege and audited?

Adversarial inputs for computer vision

Small and human-imperceptible changes can cause large misclassifications. In fraud detection, access control, or medical imaging, this means the system sees the wrong thing on demand. Models trained without robustness checks or deployed without runtime monitoring are easy targets.

What to test

• How does accuracy change under common adversarial perturbations and corruptions?
• Do you use preprocessing, ensemble checks, or confidence thresholds to fail safely?
• Can you detect distribution shift and roll back to a known-good model automatically?

Why traditional controls miss these issues

Conventional AppSec assumes deterministic software and static inputs. AI systems are probabilistic, data-driven, and adapt over time. That means your risk lives in model behavior, data lineage, and cross-component workflows instead of just in the code. If you don’t test behavior under attack conditions, you’ll pass every scanner and still fail in production. Here’s what it means for your business when models fail:

• Regulatory and compliance risk: AI errors can trigger GDPR violations (data leakage, profiling without basis) and enforcement under emerging AI regulations (e.g., the EU AI Act’s expectations for risk management, transparency, and oversight). Expect fines, mandated fixes, and audit scrutiny if you can’t show testing evidence.
• Financial and operational disruption: Bad decisions propagate quickly, such as fraud approvals, blocked transactions, misrouted logistics, or patient-care delays. Incident response for AI systems is slower without reproducible pipelines and signed artifacts.
• Reputational damage and trust erosion: Customers lose confidence when outputs are biased, unsafe, or easily manipulated. Recovery takes longer for AI failures because explanations and corrective actions require deeper transparency.

A practical AI security testing program maps attack surfaces, runs adversarial tests pre-release and in CI, and validates guardrails in production. It treats data as code with provenance, signs and verifies training artifacts, isolates high-risk model actions, and monitors behavior for drift and abuse. Most importantly, it ties results to business impact so leaders can decide what to fix now and what to defer with eyes open.

Test like an attacker, Validate like an engineer

Traditional pen tests don’t cover how AI actually fails in production. Models behave probabilistically, change with new data, and interact with tools and external content. You’ll miss issues that cost you money, slow delivery, and create compliance exposure if you keep on waiting for annual testing. Here’s an idea: how about treating your AI model testing as part of your SDLC instead of a one-off assessment?

This means:

• Testing model inputs with adversarial examples
• Probing for prompt injection vulnerabilities
• Attempting data extraction through side channels
• Validating model behavior under unexpected conditions

Your security team probably doesn't have these skills yet. That's a problem you need to fix immediately.

Shift left: make AI model testing part of DevSecOps

Fold model testing into the same loops your teams already use. You’ll catch issues earlier, fix them faster, and document controls automatically.

Make this standard practice

• Add red-team prompt suites, adversarial inputs, and poison canaries to CI for every model change.
• Gate releases on behavioral metrics (attack success rate, jailbreak rate, data-leak rate) alongside accuracy.
• Version and sign datasets, training code, and model artifacts; fail builds if provenance checks don’t match.
• Run chaos tests in staging: vary temperature/seeds, simulate bad retrievals, and throttle tool calls to verify safe failure.
• Log model inputs/outputs with privacy controls and attach run IDs to tickets for reproducibility and audits.

Key Objectives of Testing

Your AI security testing program needs clear objectives:

1. Detect susceptibility to adversarial inputs: Can small, targeted changes to inputs cause catastrophic failures?
2. Ensure robustness against data manipulation: How does your model behave when data is corrupted, poisoned, or biased?
3. Stress test explainability and consistency: Can you explain why your model made a specific decision? Does it behave predictably?
4. Validate privacy preservation: Can sensitive training data be extracted through careful querying?

Attack like an adversary, then demand engineering-grade proof of control: signed artifacts, reproducible runs, thresholds tied to business risk, and tickets with concrete fixes. When security and product share the same dashboards and gates, you reduce incident risk without slowing releases.

Techniques for breaking AI models safely

You need to break your models before attackers do. Here's how to do it systematically and safely.

Adversarial input generation

Adversarial examples are inputs specifically designed to cause AI models to fail. They exploit the mathematical foundations of machine learning to create outputs that humans would never mistake.

Gradient-based attacks (FGSM, PGD) explained

Techniques like Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) work by calculating the gradient of the model's loss function with respect to the input, then modifying the input in the direction that maximizes error.

In plain English: they find the smallest change that causes the biggest failure.

Use FGSM for quick screening and PGD for stronger and more realistic stress tests. Track attack success rate, confidence drop, and how often defenses trigger.

Data poisoning simulations

Data poisoning attacks target the training process itself. They're harder to detect because they don't look like attacks during inference; instead, they're baked into the model's behavior.

Injecting corrupted data into training pipelines

Poisoning changes how a model learns. You simulate this by inserting a small set of crafted samples into training or fine-tuning data. Options include backdoors (a trigger pattern forces a target label), label flips (clean-label attacks that look legitimate), and retrieval poisoning (for RAG, planting tainted content the model will consume). After training, you probe for trigger activation, unusual memorization, or sharp accuracy shifts on targeted classes. Measure backdoor trigger rate, clean accuracy delta, and drift from a signed baseline.

Use this short and repeatable checklist to keep poisoned data out and prove diligence:

1. Provenance: Every dataset has a source, owner, license, and hash; manifests are signed.
2. Schema & sanity: Enforce schema checks, deduplication, and outlier detection on ingest.
3. Label quality: Sample labels for human review; compare annotator agreement and spot flips.
4. Canaries: Plant known canary samples; alert if the model memorizes them verbatim.
5. Drift watch: Compare feature/label distributions to the last approved snapshot.
6. Quarantine: Any anomaly routes to a review queue, not straight into training.

If a 0.1% poisoning rate can compromise your model, then what does that say about your security?

Model extraction and inference testing

Attackers can steal your access directly or extract sensitive information through careful querying. They won’t even need to access your model directly.

Membership inference attacks

These attacks determine whether specific data was used to train a model. This is a privacy nightmare if your model was trained on sensitive information.

Test by:

1. Creating shadow models that mimic your target model
2. Training them on known datasets
3. Measuring confidence differences between data that was and wasn't in the training set

Model stealing through query-based extraction

Attackers can recreate your model's functionality by observing its outputs on carefully crafted inputs. This threatens your intellectual property and enables further attacks.

Test by:

1. Querying your model with boundary cases
2. Using the outputs to train a substitute model
3. Measuring how closely the substitute matches your original

AI model testing can introduce its own risks if you’re not careful. That’s why it’s important to treat the process with the same discipline as any other security control. Testing should happen in controlled environments, instead of using live customer data or external services without clear approval.

Building an AI Security Testing Workflow

No wonder you find issues late in your security testing when it's outside your ML lifecycle. Not to mention that it’s also the time when fixes are expensive, outages are public, and auditors start asking for evidence. 

Ad-hoc testing isn't enough. You need a systematic approach that integrates with your existing development processes.

Pre-deployment: automate adversarial testing in CI/CD

Treat each model change like a code change. Automated pipelines can run adversarial input tests, data validation checks, and baseline comparisons as part of CI/CD. Promotion decisions are based on clear metrics (like jailbreak success rate or data-leak likelihood) rather than assumptions. That keeps issues out of production and gives you an auditable record of what was tested.

Automate this

• Adversarial input suites for your modality (FGSM/PGD+corruptions for vision; prompt injection/jailbreak and tool-misuse tests for LLMs; poisoning canaries for training data).
• Baseline vs. candidate comparisons with hard fail thresholds on safety, robustness, and consistency.
• Provenance checks (hashes, manifests) for data and models; block if they don’t match.

What you get

• Fewer regressions reaching staging and prod.
• Faster triage with reproducible failures tied to a specific commit or model version.
• Audit-ready evidence that robustness and safety were tested before release.

Post-deployment: run safe and repeatable red-team exercises

Models behave differently once real users and data are in play, and that’s why red-team exercises are valuable after deployment. Done in controlled environments, these tests simulate real-world attacks such as prompt injection or model extraction. The goal here is to understand how resilient your system is and whether guardrails, monitoring, and fail-safes are actually working.

Make this standard

• Quarterly scenario packs that reflect your highest-impact risks (PII leakage, tool abuse, business logic bypass).
• Clear stop conditions, data handling rules, and an incident path if tests surface real exposure.
• Findings routed to owners with SLAs and tracked to closure in your ticketing system.

What you get

• Real assurance on the paths attackers actually use.
• Continuous hardening of prompts, retrieval sources, and tool scopes without guesswork.
• Proof that controls hold up under pressure.

Tooling and frameworks

Open source gives you breadth and transparency, but commercial and cloud platforms give you scale and integrations. Use both. Open tools in CI for repeatable tests, and managed platforms where you need enterprise controls and reporting.

Open-source options

• Adversarial Robustness Toolbox (ART): IBM's comprehensive framework for adversarial machine learning
• Foolbox: Python library for creating adversarial examples
• TextAttack: Framework for generating adversarial examples for NLP models
• CleverHans: TensorFlow library implementing attacks against neural networks

Commercial platforms

For enterprise-scale testing:

• Cloud provider solutions (AWS, Azure, GCP) with built-in ML security features
• Specialized AI security platforms with continuous monitoring
• Managed red team services with AI expertise

The investment is worth it. The alternative is finding out about vulnerabilities from attackers.

Roles and responsibilities

Who owns what (without turf wars)

• Security/AppSec: define risk budgets and policy, own red-team playbooks, select guardrails, and run compliance evidence. They approve exceptions with expiry and follow up on SLAs.
• Data Science/ML Engineering: implement test harnesses, add evals to training/CI, manage data provenance, and fix model/pipeline issues found by tests.
• Platform/SRE: enforce runtime controls (rate limits, isolation, secrets), ensure logging/observability, and operate kill switches.
• Product/Legal/Privacy: set business impact thresholds, review sensitive use cases, and ensure regulatory alignment.

Collaboration that doesn’t slow teams down

• One shared dashboard for risk metrics per model (attack success, leak rate, drift, time-to-fix).
• Release gates owned by engineering, policy owned by security that are both visible in CI.
• A lightweight exception workflow: owner, rationale, compensating controls, expiry, and review date.

The Cost of Inaction

The cost of proper testing is a fraction of what you'll pay for a major AI security incident. Data breaches, regulatory fines, lost customers, and damaged reputation far outweigh the investment in prevention.

The next step is simple. Ask yourself these questions:

• Are adversarial and red-team tests part of your lifecycle, or ad hoc?
• Can you show evidence of testing if regulators or auditors ask tomorrow?
• Do your security and data science teams have clear ownership for AI resilience?

If your answer to any of these is NO, then you’re relying on luck all this time. we45’s AI security services are built to help you expose weaknesses safely, build repeatable testing practices, and give you the evidence you’ll need when it matters most.

Start breaking your models today. Or wait for attackers to do it for you.

The choice is yours. But the consequences aren't.