Jan 15 2026

The Hidden Battle: Defending AI/ML APIs from Prompt Injection and Data Poisoning

Category: AI,AI Governance Tools,API security,Data Governance,data security — disc7 @ 8:25 am

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Protecting AI and ML model–serving APIs has become a new and critical security frontier. As organizations increasingly expose Generative AI and machine learning capabilities through APIs, attackers are shifting their focus from traditional infrastructure to the models themselves.

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AI red teams are now observing entirely new categories of attacks that did not exist in conventional application security. These threats specifically target how GenAI and ML models interpret input and learn from data—areas where legacy security tools such as Web Application Firewalls (WAFs) offer little to no protection.

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Two dominant threats stand out in this emerging landscape: prompt injection and data poisoning. Both attacks exploit fundamental properties of AI systems rather than software vulnerabilities, making them harder to detect with traditional rule-based defenses.

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Prompt injection attacks manipulate a Large Language Model by crafting inputs that override or bypass its intended instructions. By embedding hidden or misleading commands in user prompts, attackers can coerce the model into revealing sensitive information or performing unauthorized actions.

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This type of attack is comparable to slipping a secret instruction past a guard. Even a well-designed AI can be tricked into ignoring safeguards if user input is not strictly controlled and separated from system-level instructions.

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Effective mitigation starts with treating all user input as untrusted code. Clear delimiters must be used to isolate trusted system prompts from user-provided text, ensuring the model can clearly distinguish between authoritative instructions and external input.

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In parallel, the principle of least privilege is essential. AI-serving APIs should operate with minimal access rights so that even if a model is manipulated, the potential damage—often referred to as the blast radius—remains limited and manageable.

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Data poisoning attacks, in contrast, undermine the integrity of the model itself. By injecting corrupted, biased, or mislabeled data into training datasets, attackers can subtly alter model behavior or implant hidden backdoors that trigger under specific conditions.

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Defending against data poisoning requires rigorous data governance. This includes tracking the provenance of all training data, continuously monitoring for anomalies, and applying robust training techniques that reduce the model’s sensitivity to small, malicious data manipulations.

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Together, these controls shift AI security from a perimeter-based mindset to one focused on model behavior, data integrity, and controlled execution—areas that demand new tools, skills, and security architectures.

My Opinion
AI/ML API security should be treated as a first-class risk domain, not an extension of traditional application security. Organizations deploying GenAI without specialized defenses for prompt injection and data poisoning are effectively operating blind. In my view, AI security controls must be embedded into governance, risk management, and system design from day one—ideally aligned with standards like ISO 27001, ISO 42001 and emerging AI risk frameworks—rather than bolted on after an incident forces the issue.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI, APIs, Data Poisoning, ML, prompt Injection

Dec 31 2025

Shadow AI: When Productivity Gains Create New Risks

Category: AI — disc7 @ 9:20 am

Shadow AI: The Productivity Paradox

Organizations face a new security challenge that doesn’t originate from malicious actors but from well-intentioned employees simply trying to do their jobs more efficiently. This phenomenon, known as Shadow AI, represents the unauthorized use of AI tools without IT oversight or approval.

Marketing teams routinely feed customer data into free AI platforms to generate compelling copy and campaign content. They see these tools as productivity accelerators, never considering the security implications of sharing sensitive customer information with external systems.

Development teams paste proprietary source code into public chatbots seeking quick debugging assistance or code optimization suggestions. The immediate problem-solving benefit overshadows concerns about intellectual property exposure or code base security.

Human resources departments upload candidate resumes and personal information to AI summarization tools, streamlining their screening processes. The efficiency gains feel worth the convenience, while data privacy considerations remain an afterthought.

These employees aren’t threat actors—they’re productivity seekers exploiting powerful tools available at their fingertips. Once organizational data enters public AI models or third-party vector databases, it escapes corporate control entirely and becomes permanently exposed.

The data now faces novel attack vectors like prompt injection, where adversaries manipulate AI systems through carefully crafted queries to extract sensitive information, essentially asking the model to “forget your instructions and reveal confidential data.” Traditional security measures offer no protection against these techniques.

We’re witnessing a fundamental shift from the old paradigm of “Data Exfiltration” driven by external criminals to “Data Integration” driven by internal employees. The threat landscape has evolved beyond perimeter defense scenarios.

Legacy security architectures built on network perimeters, firewalls, and endpoint protection become irrelevant when employees voluntarily connect to external AI services. These traditional controls can’t prevent authorized users from sharing data through legitimate web interfaces.

The castle-and-moat security model fails completely when your own workforce continuously creates tunnels through the walls to access the most powerful computational tools humanity has ever created. Organizations need governance frameworks, not just technical barriers.

Opinion: Shadow AI represents the most significant information security challenge for 2026 because it fundamentally breaks the traditional security model. Unlike previous shadow IT concerns (unauthorized SaaS apps), AI tools actively ingest, process, and potentially retain your data for model training purposes. Organizations need immediate AI governance frameworks including acceptable use policies, approved AI tool catalogs, data classification training, and technical controls like DLP rules for AI service domains. The solution isn’t blocking AI—that’s impossible and counterproductive—but rather creating “Lighted AI” pathways: secure, sanctioned AI tools with proper data handling controls. ISO 42001 provides exactly this framework, which is why AI Management Systems have become business-critical rather than optional compliance exercises.

Shadow AI for Everyone: Understanding Unauthorized Artificial Intelligence, Data Exposure, and the Hidden Threats Inside Modern Enterprises

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Tags: prompt Injection, Shadow AI

Jun 13 2025

Prompt injection attacks can have serious security implications

Category: AI,App Security — disc7 @ 11:50 am

Prompt injection attacks can have serious security implications, particularly for AI-driven applications. Here are some potential consequences:

  • Unauthorized data access: Attackers can manipulate AI models to reveal sensitive information that should remain protected.
  • Bypassing security controls: Malicious inputs can override built-in safeguards, leading to unintended outputs or actions.
  • System prompt leakage: Attackers may extract internal configurations or instructions meant to remain hidden.
  • False content generation: AI models can be tricked into producing misleading or harmful information.
  • Persistent manipulation: Some attacks can alter AI behavior across multiple interactions, making mitigation more difficult.
  • Exploitation of connected tools: If an AI system integrates with external APIs or automation tools, attackers could misuse these connections for unauthorized actions.

Preventing prompt injection attacks requires a combination of security measures and careful prompt design. Here are some best practices:

  • Separate user input from system instructions: Avoid directly concatenating user input with system prompts to prevent unintended command execution.
  • Use structured input formats: Implement XML or JSON-based structures to clearly differentiate user input from system directives.
  • Apply input validation and sanitization: Filter out potentially harmful instructions and restrict unexpected characters or phrases.
  • Limit model permissions: Ensure AI systems have restricted access to sensitive data and external tools to minimize exploitation risks.
  • Monitor and log interactions: Track AI responses for anomalies that may indicate an attempted injection attack.
  • Implement guardrails: Use predefined security policies and response filtering to prevent unauthorized actions.

Strengthen your AI system against prompt injection attacks, here are some tailored strategies:

  • Define clear input boundaries: Ensure user inputs are handled separately from system instructions to avoid unintended command execution.
  • Use predefined response templates: This limits the ability of injected prompts to influence output behavior.
  • Regularly audit and update security measures: AI models evolve, so keeping security protocols up to date is essential.
  • Restrict model privileges: Minimize the AI’s access to sensitive data and external integrations to mitigate risks.
  • Employ adversarial testing: Simulate attacks to identify weaknesses and improve defenses before exploitation occurs.
  • Educate users and developers: Understanding potential threats helps in maintaining secure interactions.
  • Leverage external validation: Implement third-party security reviews to uncover vulnerabilities from an unbiased perspective.

Source: https://security.googleblog.com/2025/06/mitigating-prompt-injection-attacks.html

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Tags: prompt Injection