The 10 Most Critical AI Security Risks in 2025 (And How to Defend Against Them)

Generative AI adoption is booming, but so are the attack vectors. As enterprises push LLMs and autonomous agents into production, the risks go beyond hallucinations or compliance missteps. We're seeing real adversaries exploit blind spots in AI pipelines to exfiltrate data, poison training sets, and hijack AI-powered infrastructure.

This isn’t theoretical anymore.

From Fortune 500 red teams to rogue API abuse, the risks are now business-critical. That’s why we’re breaking down the 10 most pressing AI security threats of 2025, and what your team can do to get ahead of them.

Whether you’re a CISO, security engineer, or AI platform architect, this list is your action plan.

1. Prompt Injection (Still #1 in 2025)

Despite growing awareness, prompt injection remains the most exploited LLM attack vector. It allows attackers to override model behavior, leak confidential data, or execute malicious instructions by manipulating input.

This vulnerability exploits the core mechanism of how LLMs interpret natural language, which makes it deceptively simple to launch and difficult to fully patch. In real-world scenarios, prompt injections have enabled attackers to circumvent filters, impersonate users, or even hijack autonomous agents operating within enterprise workflows.

How to Defend:

• Input/output filtering and canonicalization: Sanitize inputs to strip out hidden instructions or encoded payloads. Normalize prompts before execution to prevent unintended instruction chaining.
• Use Guardrails like NeuralTrust’s Gateway: Implement gateway-level protections to screen requests for known attack patterns and enforce context-aware policies.
• Perform adversarial prompt testing using red teaming tools: Regular red teaming uncovers edge-case vulnerabilities and helps keep your defenses ahead of adversarial innovation.

2. Model Inversion Attacks

Attackers query a model and reconstruct sensitive training data, such as PII or IP. Particularly dangerous for models trained on internal datasets.

Model inversion isn’t just theoretical. Several academic and real-world cases have demonstrated successful reconstruction of faces, email addresses, and even source code from AI responses. In regulated industries like healthcare or finance, this can trigger compliance failures and significant data breach penalties.

How to Defend:

• Use differential privacy or federated training methods: These techniques limit the ability of any single output to reveal individual training examples.
• Limit model output verbosity: The less detail provided, the harder it is to reverse-engineer training data.
• Detect abnormal query patterns: Track for iterative queries that suggest reconstruction attempts, especially from automated sources.

NIST’s Risk Management Framework flags this as a top “privacy and confidentiality” concern.

3. Supply Chain Poisoning (Model or Dataset)

Open-source LLMs or libraries can be compromised during distribution or training. An attacker introduces malicious weights, backdoors, or biased data.

As the AI ecosystem matures, the supply chain expands, and so do the opportunities for attackers. We've seen incidents of corrupted Hugging Face repositories, contaminated training datasets, and malicious pre-trained models spreading silently across organizations.

How to Defend:

• Verify model lineage: Always confirm where your models and datasets originate and how they were trained.
• Apply secure hashing + digital signatures on datasets: Ensure integrity with checksums and signed provenance tracking.
• Use red team validation on third-party models: Before integrating external models, test them rigorously for anomalous behavior.

See also: AI Safety & Supply Chain Security (CSA)

4. LLM API Abuse & Rate-Based Attacks

As LLMs become central to digital services, from customer support to autonomous agents, malicious actors are probing these APIs at scale. Without robust traffic control, a single bad actor can overwhelm infrastructure, steal model logic, or abuse content generation.

Public-facing LLM endpoints (e.g., OpenAI, Anthropic) are now prime targets for:

• Prompt abuse (via chatbots)
• Model extraction
• Spam/jailbreak testing

How to Defend:

• Rate-limit based on behavioral analysis: Go beyond IP rate limits. Use intent-based throttling to block patterns of misuse.
• Monitor token and context window anomalies: Spikes in token usage or long-context prompts can indicate scraping or abuse.
• Use identity-aware gateways (e.g. NeuralTrust’s Firewall Comparison Tool): Tie LLM access to user roles, risk scores, or identity providers.

5. Jailbreaking via Synthetic Prompts

Even the best guardrails can be evaded. Jailbreaking uses prompt crafting (e.g., role-playing, base64 encoding) to bypass controls and access restricted model capabilities.

Attackers now share jailbreak recipes via forums and GitHub repos. Some techniques use nested prompts, fictional scenarios, or code obfuscation to trick the model into ignoring system-level restrictions. And with new LLMs launching weekly, jailbreak surface areas are expanding.

How to Defend:

• Continuously test jailbreak scenarios: Stay ahead of attackers by simulating their methods regularly.
• Update jailbreak mitigation policies weekly: Treat jailbreak patterns like malware signatures. Review and update constantly.
• Monitor community-reported jailbreaks (e.g. GPTZero’s Jailbreak Tracker): Leverage OSINT to discover emerging techniques.

6. Shadow AI Tools in the Enterprise

Employees increasingly bring in unauthorized LLMs or AI apps (e.g. Chrome extensions, low-code agents) to “get things done faster.” These shadow tools can leak data or execute unknown logic.

This mirrors the rise of “shadow IT” in the SaaS era, but with far more dangerous implications. A Chrome extension with LLM integration might store prompts in the cloud, or a no-code agent might act autonomously on sensitive data.

How to Defend:

• Deploy AI observability platforms: Monitor traffic for unauthorized AI tool usage and anomaly detection.
• Restrict outbound AI traffic with policy-based enforcement: Use firewalls or CASBs to prevent unknown tools from reaching external LLMs.
• Educate staff with AI usage guidelines: Employees aren’t malicious; they’re often unaware. Clear rules go a long way.

See: NeuralTrust’s Observability Module

7. Adversarial Prompt Engineering

Advanced attackers create input prompts designed to subtly shift a model’s behavior without obvious override commands. This is a rising concern for fine-tuned internal models.

These attacks are subtle: not overt jailbreaks, but clever manipulations of tone, context, or structure that nudge the model toward undesired outputs. In high-stakes environments (e.g., finance, legal), the implications can be serious.

How to Defend:

• Test for implicit adversarial prompts: Use fuzzing tools or adversarial input generators to explore edge cases.
• Use ensemble methods and confidence scoring: Compare outputs across multiple models to detect suspicious variations.
• Incorporate interpretability tools (e.g., LIME, SHAP): Understand why a model chose its response, and when something feels off.

8. Over-Permissive Fine-Tuned Models

Fine-tuned enterprise models often skip robust permissioning. Teams accidentally give models too much access: to HR data, private documents, or decision APIs.

In the rush to deploy LLMs internally, access boundaries are frequently overlooked. One misconfigured endpoint can give a chatbot access to payroll data or sensitive contracts.

How to Defend:

• Use capability isolation (one model per task): Don’t let a helpdesk bot access internal finance tools.
• Perform periodic access reviews: Audit what your models can access and adjust as needed.
• Log and monitor sensitive queries: Track usage patterns for signs of privilege creep or abuse.

9. Model Theft via API Probing

With enough queries, attackers can replicate model behavior, even if they can't see the weights. This is particularly dangerous for proprietary internal models.

This technique, known as model extraction, allows adversaries to create a surrogate model that mimics your proprietary LLM. That surrogate can then be fine-tuned, monetized, or used to develop attacks against your system.

How to Defend:

• Use watermarking techniques: Embed invisible signatures into model responses to prove ownership.
• Limit high-fidelity output access: Don’t expose detailed reasoning or internal context unless absolutely necessary.
• Detect model copying behavior with traffic heuristics: Look for telltale signs: repeated prompts, high-frequency probing, and unusual patterns.

Want to dive deeper? Check: Understanding & Preventing Model Theft

10. AI-Specific Denial of Service (DoS)

Attackers are beginning to target the unique compute bottlenecks of AI systems, such as token inflation, large context abuse, or GPU resource starvation.

Unlike traditional DoS attacks, these exploits use valid inputs to intentionally overload the AI system. Large context prompts or recursive chaining can spike compute costs and slow down response times for all users.

How to Defend:

• Use input/output constraints per org/user: Set hard limits on context length, frequency, and payload size.
• Offload long context prompts to side processing pipelines: Don’t let user-facing systems handle unbounded compute loads.
• Prioritize multi-tenant isolation in AI infrastructure: A bad actor in one tenant shouldn’t impact others.

Securing the Stack: 2025 and Beyond

It’s no longer enough to “monitor your LLM.” Security teams must treat generative AI infrastructure like any other attack surface, including layered defenses, red team testing, access control, and incident response workflows.

How NeuralTrust Can Help:

• Real-time traffic firewall for AI models: Block malicious inputs before they reach your models.
• Evaluation and adversarial benchmarking: Know how your model performs under attack, and improve it continuously.
• Full-stack observability + reporting: Monitor everything from prompt to GPU usage, in real time.

Learn more: AI Gateway: Centralized AI Management at Scale

Final Thoughts

AI systems are transforming the enterprise, but they’re also introducing new risks at an unprecedented pace. From prompt injection to adversarial probing, defending LLMs in 2025 will require a dedicated security mindset, not just model tuning.

Start by understanding your threat model. Then act with the right tools, controls, and frameworks.

Because if you’re not thinking about these 10 risks… someone else probably is.