DISC InfoSec blogAdversarial AI Attacks Archives

Jul 17 2025

Securing AI from Within: How to Defend Against Prompt Injection Attacks

Category: AI — disc7 @ 9:29 am

Prompt injection attacks are a rising threat in the AI landscape. They occur when malicious instructions are embedded within seemingly innocent user input. Once processed by an AI model, these instructions can trigger unintended and dangerous behavior—such as leaking sensitive information or generating harmful content. Traditional cybersecurity defenses like firewalls and antivirus tools are powerless against these attacks because they operate at the application level, not the content level where AI vulnerabilities lie.

A practical example is asking a chatbot to summarize an article, but the article secretly contains instructions that override the intended behavior of the AI—like requesting sensitive internal data or malicious actions. Without specific safeguards in place, many AI systems follow these hidden prompts blindly. This makes prompt injection not only technically alarming but a serious business liability.

To counter this, AI security proxies are emerging as a preferred solution. These proxies sit between the user and the AI model, inspecting both inputs and outputs for harmful instructions or data leakage. If a prompt is malicious, the proxy intercepts it before it reaches the model. If the AI response includes sensitive or inappropriate content, the proxy can block or sanitize it before delivery.

AI security proxies like Llama Guard use dedicated models trained to detect and neutralize prompt injection attempts. They offer several benefits: centralized protection for multiple AI systems, consistent policy enforcement across different models, and a unified dashboard to monitor attack attempts. This approach simplifies and strengthens AI security without retraining every model individually.

Relying solely on model fine-tuning to resist prompt injections is insufficient. Attackers constantly evolve their tactics, and retraining models after every update is both time-consuming and unreliable. Proxies provide a more agile and scalable layer of defense that aligns with the principle of defense in depth—an approach that layers multiple controls for stronger protection.

More than a technical issue, prompt injection represents a strategic business risk. AI systems that leak data or generate toxic content can trigger compliance violations, reputational harm, and financial loss. This is why prompt injection mitigation should be built into every organization’s AI risk management strategy from day one.

Opinion & Recommendation:
To effectively counter prompt injection, organizations should adopt a layered defense model. Start with strong input/output filtering using AI-aware security proxies. Combine this with secure prompt design, robust access controls, and model-level fine-tuning for context awareness. Regular red-teaming exercises and continuous threat modeling should also be incorporated. Like any emerging threat, proactive governance and cross-functional collaboration will be key to building AI systems that are secure by design.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

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Tags: Adversarial AI Attacks, AI Prompt Injection

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Feb 12 2025

Some AI frameworks have remote code execution as a feature – explore common attack vectors and mitigation strategies

Category: AI,Remote code — disc7 @ 7:45 am

Some AI frameworks and platforms support remote code execution (RCE) as a feature, often for legitimate use cases like distributed computing, model training, and inference. However, this can also pose security risks if not properly secured. Here are some notable examples:

1. AI Frameworks with Remote Execution Features

A. Jupyter Notebooks

Jupyter supports remote kernel execution, allowing users to run code on a remote server while interacting via a local browser.
If improperly configured (e.g., running on an open network without authentication), it can expose an unauthorized RCE risk.

B. Ray (for Distributed AI Computing)

Ray allows distributed execution of Python tasks across multiple nodes.
It enables remote function execution (@ray.remote) for parallel processing in machine learning workloads.
Misconfigured Ray clusters can be exploited for unauthorized code execution.

C. TensorFlow Serving & TorchServe

These frameworks execute model inference remotely, often exposing APIs for inference requests.
If the API allows arbitrary input (e.g., executing scripts inside the model environment), it can lead to RCE vulnerabilities.

D. Kubernetes & AI Workloads

AI workloads are often deployed in Kubernetes clusters, which allow remote execution via kubectl exec.
If Kubernetes RBAC is misconfigured, attackers could execute arbitrary code on AI nodes.

2. Platforms Offering Remote Code Execution

A. Google Colab

Allows users to execute Python code on remote GPUs/TPUs.
Though secure, running untrusted notebooks could execute malicious code remotely.

B. OpenAI API, Hugging Face Inference API

These platforms run AI models remotely and expose APIs for users.
They don’t expose direct RCE, but poorly designed API endpoints could introduce security risks.

3. Security Risks & Mitigations

Risk	Mitigation
Unauthenticated remote access (e.g., Jupyter, Ray)	Enable authentication & restrict network access
Arbitrary code execution via AI APIs	Implement input validation & sandboxing
Misconfigured Kubernetes clusters	Enforce RBAC & limit exec privileges
Untrusted model execution (e.g., Colab, TorchServe)	Run models in isolated environments

Securing AI Workloads Against Remote Code Execution (RCE) Risks

AI workloads often involve remote execution of code, whether for model training, inference, or distributed computing. If not properly secured, these environments can be exploited for unauthorized code execution, leading to data breaches, malware injection, or full system compromise.

1. Common AI RCE Attack Vectors & Mitigation Strategies

Attack Vector	Risk	Mitigation
Jupyter Notebook Exposed Over the Internet	Unauthorized access to the environment, remote code execution	✅ Use strong authentication (token-based or OAuth) ✅ Restrict access to trusted IPs ✅ Disable root execution
Ray or Dask Cluster Misconfiguration	Attackers can execute arbitrary functions across nodes	✅ Use firewall rules to limit access ✅ Enforce TLS encryption between nodes ✅ Require authentication for remote task execution
Compromised Model File (ML Supply Chain Attack)	Malicious models can execute arbitrary code on inference	✅ Scan models for embedded scripts ✅ Run inference in an isolated environment (Docker/sandbox)
Unsecured AI APIs (TensorFlow Serving, TorchServe)	API could allow command injection through crafted inputs	✅ Implement strict input validation ✅ Run API endpoints with least privilege
Kubernetes Cluster with Weak RBAC	Attackers gain access to AI pods and execute commands	✅ Restrict `kubectl exec` privileges ✅ Use Kubernetes Network Policies to limit communication ✅ Rotate service account credentials
Serverless AI Functions (AWS Lambda, GCP Cloud Functions)	Code execution environment can be exploited via unvalidated input	✅ Use IAM policies to restrict execution rights ✅ Validate API payloads before execution

2. Best Practices for Securing AI Workloads

A. Secure Remote Execution in Jupyter Notebooks

Jupyter Notebooks are often used for AI development and testing but can be exploited if left exposed.

🔹 Recommended Configurations:
✅ Enable password authentication:

bashCopyEditjupyter notebook --generate-config

Edit jupyter_notebook_config.py:

pythonCopyEditc.NotebookApp.password = 'hashed_password'

✅ Restrict access to localhost (--ip=127.0.0.1)
✅ Run Jupyter inside a container (Docker, Kubernetes)
✅ Use VPN or SSH tunneling instead of exposing ports

B. Lock Down Kubernetes & AI Workloads

Many AI frameworks (TensorFlow, PyTorch, Ray) run in Kubernetes, where misconfigurations can lead to container escapes and lateral movement.

🔹 Key Security Measures:
✅ Restrict kubectl exec privileges to prevent unauthorized command execution:

yamlCopyEditapiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: default
  name: restrict-exec
rules:
- apiGroups: [""]
  resources: ["pods/exec"]
  verbs: ["get"]

✅ Enforce Pod Security Policies (disable privileged containers, enforce seccomp profiles)
✅ Limit AI workloads to isolated namespaces

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

Tags: Adversarial AI Attacks, AI framwork, Remote Code Execution

Comments (3)

Oct 11 2024

To fight AI-generated malware, focus on cybersecurity fundamentals

Category: AI — disc7 @ 8:08 am

AI-powered malware is increasingly adopting AI capabilities to improve traditional cyberattack techniques. Malware such as BlackMamba and EyeSpy leverage AI for activities like evading detection and conducting more sophisticated phishing attacks. These innovations are not entirely new but represent a refinement of existing malware strategies.

While AI enhances these attacks, its greatest danger lies in the automation of simple, widespread threats, potentially increasing the volume of attacks. To combat this, businesses need strong cybersecurity practices, including regular updates, training, and the integration of AI in defense systems for faster threat detection and response.

As with the future of AI-powered threats, AI’s impact on cybersecurity practitioners is likely to be more of a gradual change than an explosive upheaval. Rather than getting swept up in the hype or carried away by the doomsayers, security teams are better off doing what they’ve always done: keeping an eye on the future with both feet planted firmly in the present.

For more details, visit the IBM article.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

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Tags: Adversarial AI Attacks, AI-generated malware, ChatGPT for Cybersecurity

Comments (7)

Oct 03 2024

AI security bubble already springing leaks

Category: AI — disc7 @ 1:17 pm

AI security bubble already springing leaks

The article highlights how the AI boom, especially in cybersecurity, is already showing signs of strain. Many AI startups, despite initial hype, are facing financial challenges, as they lack the funds to develop large language models (LLMs) independently. Larger companies are taking advantage by acquiring or licensing the technologies from these smaller firms at a bargain.

AI is just one piece of the broader cybersecurity puzzle, but it isn’t a silver bullet. Issues like system updates and cloud vulnerabilities remain critical, and AI-only security solutions may struggle without more comprehensive approaches.

Some efforts to set benchmarks for LLMs, like NIST, are underway, helping to establish standards in areas such as automated exploits and offensive security. However, AI startups face increasing difficulty competing with big players who have the resources to scale.

For more information, you can visit here