6 Critical AI Security Threats and How to Defend Against Them

Nowadays, AI powers everything from automated diagnostics to mission-critical IT operations. At the same time it creates attack surfaces that are undetectable to traditional defenses since these vulnerabilities are tied to how models learn, process inputs, and handle data. In this article, we explore six critical ways attackers can exploit models from design to production and map out the technical controls needed at each stage to ensure safety: design, training, deployment, and monitoring, to keep your AI resilient. 

Adversarial attacks – Tiny Tweaks, Big Consequences 

One common way adversaries attack AI systems is by tweaking inputs to force the model into confident mispredictions. Those attacks take advantage of a fundamental weakness in how AI models process input data. 

For clarity, here’s the attack flow in steps: 

1. Generate small, targeted changes. For images, this could be pixel-level noise; for text, subtle word swaps. Base these changes on initial model queries. 
2. Send these perturbed inputs through the prediction endpoint just as one would send any legitimate request. 
3. Record the model’s labels or confidence scores in response to each variation. 
4. Adjust the perturbations based on those responses, until reaching a barely noticeable change in the wrong prediction. 

To a human the altered input looks identical to the original, but to the model it’s enough to push the input across its decision boundary, leading to errors like mislabeling a stop sign, approving a fraudulent transaction, letting harmful content bypass a filter, or mis-triaging a patient.  

One famous example involved a machine learning model trained to recognize road signs in autonomous vehicles. Researchers demonstrated how small stickers barely noticeable to a driver placed on a stop sign could cause the model to misclassify it as a speed limit sign. In a real-world setting, such a misclassification could trigger dangerous driving decisions thereby potentially causing accidents, injuring passengers or pedestrians, and even leading to loss of life. Beyond the immediate human cost, it could also result in legal liability, public trust erosion, and significant financial damages. 

Signals and Monitoring for Adversarial Activity 

Monitor for patterns that suggest probing or manipulation of the model. For example, flag cases where nearly identical inputs produce different labels which is a sign that small changes are influencing predictions. Track each caller’s activity for sequences of very small edits followed by sudden increases in the model’s confidence, as this can indicate an attempt to find and exploit decision boundaries. Combining these signals can help detect and respond to adversarial behavior before it causes harm. 

Practical Defenses against Adversarial Attacks:  

To stop such malicious tweaks from slipping through, apply these controls: 

• Edge Input Filtering: Inspect every request and reject any data outside your expected bounds-for example, ensure image pixels stay within normal color ranges or allow only words from your approved vocabulary 
• Adversarial Training: During model training, mix in known attack examples so the model learns to treat those perturbations as noise rather than real features 
• Anomaly Detection: At inference time, monitor incoming data patterns and automatically flag or block requests that look significantly different from your normal traffic 

Model Inversion and Extraction – From Responses to Reconstructions and Cloned Models 

If an attacker fails to mislead your model, they instead might turn to probing your model until they can reconstruct your training data, or build a copycat system of their own. These so-called model inversion and extraction attacks happen when an AI model’s prediction endpoint is exposed without strong access controls or output limits, essentially turning it into an open information gateway that anyone can query.  

An attacker can send hundreds or thousands of carefully varied inputs, sometimes even meaningless or random data, and watch how the model’s confidence scores or answers change. By piecing those responses together, they can work backwards to recreate sensitive examples from your training set (model inversion) or train their own copycat model that behaves just like yours (model extraction). Consider the following an illustration to better understand the difference between the two attacks: 

Left panel: The attacker sends queries to your model’s API and uses the responses to reconstruct sensitive training data.
Right panel: The attacker collects input–output pairs from the same API and trains a surrogate model that replicates your original one. 

Signals and Monitoring for Model Inversions and Extractions 

Potential signs of model inversion or extraction include unusually high query volumes from a single user or identity, as well as a high diversity of inputs from the same source that appear designed to explore a wide range of model behaviors. Another red flag is confidence score probing, where queries seem intentionally crafted to map the model’s certainty across different inputs. Finally, pay close attention to query distributions whose characteristics, such as input length, vocabulary usage, or embedding similarity, deviate significantly from normal traffic patterns, as these may indicate systematic attempts to reverse-engineer or replicate the model. 

Defenses Against Model Inversion and Extraction  

Prevent attackers from treating your API like a data faucet or clone factory by enforcing these measures: 

• Hardened API Access: Require authentication and enforce per‑user rate limits to prevent mass harvesting of input–output pairs 
• Output Hardening: Round or bucket confidence scores (or add a bit of noise) to deny attackers the precise feedback they need to reconstruct your data or clone your model 
• Probe Detection: Watch for systematic sweeps through your input space (like grid searches) in your logs, and trigger alerts when you see them 

Model DoS (Sponge Attack) – Soaking Up Your Compute 

When direct data theft fails, adversaries often shift tactics to flooding your service with overwhelming traffic. As such, model DoS (or sponge attacks) occur when an attacker floods your AI model with inputs specifically crafted to consume excessive compute and memory; think about very long texts, large image batches, or inputs that trigger expensive preprocessing. By hammering the AI Model API with these resource-intensive requests, the attacker drives up computational load and exhausts available resources, causing legitimate queries to slow down or fail entirely. An AI prediction endpoint that processes every request to completion no matter how costly can be “sponge‑attacked.”  In practice, this occurs when the service accepts unbounded inputs without upfront validation, fails to enforce size and format limits, lacks per-client rate limiting and concurrency controls, and exposes profiling or debug endpoints. 

Signals and Monitoring for Model DoS (Sponge Attacks) 

Monitor key performance metrics such as p95 and p99 latency, queue depth, token counts or sequence lengths, image dimensions, and CPU, GPU, or memory usage for each caller. Pay special attention to sudden spikes in oversized inputs, as well as increases in timeouts, 5xx error rates, or resource saturation that are concentrated among a small number of identities. These patterns can signal targeted attempts to overload or degrade your service. 

Defenses Against Model DoS (Sponge Attacks)  

Stop resource-draining requests in their tracks with these safeguards: 

• Strict Payload Constraints: Reject requests that exceed safe bounds (max text length, image dimensions, batch size) to prevent oversized or nested payloads from monopolizing resources. 
• Concurrency Controls: Cap simultaneous in‑flight requests per user or node so no single actor can exhaust CPU, GPU, or memory. 
• Early‑Exit Circuit Breakers: Run a lightweight cost estimator, using simple heuristics or a proxy model, at the edge and abort any request predicted to exceed your predefined resource threshold. 

Jailbreaking – Forcing the Model Off-Limits 

Once attackers have learned to overwhelm or probe your model, they’ll try to force it into forbidden territory, this is the essence of a jailbreaking attack. In a jailbreaking, adversaries aim to subvert the filters and guardrails designed to keep the model’s output within acceptable bounds. They craft prompts that, on the surface, appear harmless or compliant to probe the moderation logic for weaknesses. As these probes produce answers, attackers improve their wording and layout, easily including disallowed content in questions that look safe. A human reviewer might think a question looks harmless, but it can still bypass policy checks. This can trick the model into producing restricted or malicious outputs. 

Signals and Monitoring for Jailbreaking 

Monitor for “block-then-allow” patterns, where prompts that are initially blocked are later accepted after minor rewording. Keep an eye on increasing rates of prompts that sit close to policy boundaries, as these may indicate systematic attempts to evade safeguards. Additionally, track cases where a secondary review or classifier flags an output as policy-violating even though it passed the initial filtering, as this can reveal gaps in your primary defenses. 

Defenses Against Jailbreaking 

To protect your model against jailbreaking, you need to make it difficult for attackers to find and exploit any weak spots. Protect your data by creating defense in depth: 

• Normalize & sanitize inputs: Strip HTML tags, URL encodings, control characters, and other obfuscation before filtering 
• Dual‐layer content checks: Run every prompt through both rule-based and ML-based filters; block any input that fails either 
• Integrity markers: Embed hidden tokens around system instructions; if they’re altered or removed, reject the request 
• Ongoing red teaming: Regularly challenge your filters with fresh adversarial prompts and update rules at the first sign of a bypass 

Prompt Injection – Hijacking Model Instructions 

To go even deeper, attackers don’t stop at sneaking past filters and instead rewrite the instructions you gave your model, a technique known as prompt injection. Prompt injection exploits the combination of system instructions and user-provided text on which many language model deployments are based. Attackers identify where users have entered information into the model’s instructions, often marked by special symbols or tokens and insert harmful instructions alongside them. By adding phrases such as ‘ignore previous instructions’ or ‘now reveal the secret’, they can alter the model’s behavior. This causes the model to ignore the rules it was designed to follow and start following the attacker’s commands instead. While these harmful injections may be easy for people to spot, the model’s handling of instructions can become “confused” and prioritize them. This can lead to unintended actions or the sharing of private information. Even simple formatting tricks, such as using line breaks, alternative encodings or wrapping payloads in metadata, can greatly increase the likelihood of the injection working. 

Signals and Monitoring for Prompt Injection 

Pre-scan any untrusted content, such as outputs from retrieval-augmented generation (RAG) systems, tool responses, or web pages, for patterns that could override model behavior. Raise alerts when, after processing such content, the model attempts actions it should not perform, such as making forbidden tool calls or referencing internal or system prompts. This helps detect and stop potential injection or override attacks before they can compromise the system. 

Defenses Against Prompt Injection 

The key to stopping prompt injections is to keep the system and user instructions separate and tamper-proof. 

• Strict prompt separation: Keep system directives and user text in distinct, immutable fields (e.g. separate JSON properties) 
• Control‐character stripping: Remove line breaks, homoglyphs, metadata wrappers, and other formatting tricks from user input 
• Tamper-proof wrappers: Sign or hash your system prompts; reject any request whose signature fails to verify 
• Output validation: Post‐inference, scan results against policy rules and immediately redact or block any forbidden content 

Data Poisoning – Backdoors in Your Training Data 

Unlike previous attacks, where the consequences are often immediate, data poisoning is a threat that can remain dormant for a long time before it surfaces. Data poisoning contaminates a model by tainting the training or fine-tuning dataset. In this situation, adversaries quietly add examples that are either wrongly labelled or have hidden backdoor triggers in data streams that go into the learning pipeline. When the model is retrained with this contaminated data, it learns both the good and the bad patterns. Later, when the model is being used, an attacker can present the specific trigger (a phrase or pattern hidden in the input) to activate the backdoor behavior, causing the model to misclassify or reveal confidential information. The poisoned samples are few and closely resemble real data, so they often go undetected during regular audits. The model seems to perform normally until the hidden, harmful behavior is triggered. 

Signals and Monitoring for Data Poisoning 

During the intake process, perform schema/label sanity checks, LSH deduplication and outlier/spectral signature tests to identify anomalous clusters that appear normal but contain backdoor triggers. Score the samples using influence-based metrics to highlight those that have a significant impact on the loss. After training, maintain canary/backdoor suites (e.g. trigger phrases and patches) and issue alerts for drops in performance or activation patterns consistent with known backdoor behaviours. Sudden regressions in clean evaluations while ‘mysteriously’ passing rare patterns are a red flag. 

Defenses Against Data Poisoning 

Stop malicious samples from sneaking into your training set with these checks: 

• Lock down data writes: Enforce RBAC and MFA on all training data repositories so only trusted accounts can modify them 
• Automated data vetting: Run schema checks and statistical outlier detection on every new sample before it’s ingested 
• Provenance tagging: Attach source, timestamp, and version metadata to each data point for easy trace-back and isolation 
• Scheduled backdoor audits: Periodically test with known challenge triggers; if a backdoor fires, revert to a clean checkpoint and purge tainted data 

Baseline Security Requirements – The Must Haves 

Building truly resilient AI requires making security a core priority from the start which is the essence of  our secure by design philosophy.  

Alongside the targeted measures above, keep these baseline controls in place: 

• Authentication & Authorization: Require every request to present valid credentials (API keys, OAuth tokens, or service certificates) and enforce role‑based permissions and network policies so only authorized systems or users can call your model 
• Request Rate Limiting: Restrict each client to a fixed number of calls per time window and limit simultaneous in‑flight requests per user or IP. This prevents both large‑scale probing and resource‑exhaustion floods 
• Centralized Logging & Anomaly Alerts: Capture every request, response, client identity, and payload metadata in a centralized system. Define thresholds for abnormal behavior and trigger instant notifications when they occur 

Avoid Security Flaws in AI: Build Resilience with Architecture, Automation & Audits

AI introduces a whole new class of threats. Left unchecked, these exploits can compromise everything from model accuracy to data privacy and system availability. The question isn’t whether these threats will happen, but whether your pipeline is secure by design. That means defining security requirements at architecture time, implementing them through training and deployment, and verifying them continuously in production. When controls are specified upfront, automated in the delivery pipeline, and supported by clear ownership and review cycles, you reduce exposure, accelerate detection, and make recovery disciplined rather than ad hoc.