Traditional AppSec vs. AI Security: Addressing Modern Risks

Agentic systems are transforming application development, introducing new security challenges that traditional AppSec controls can't address. During our recent webinar we discussed how cyber attackers are exploiting vulnerabilities unique to agentic systems, necessitating a fresh security approach.

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This blog explores the differences between Traditional AppSec and AI security, highlighting the new tools and controls needed to protect agentic-driven applications.

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Understanding New AI-Specific Threats

Traditional AppSec controls were designed for conventional software systems, focusing on known vulnerabilities and predictable attack patterns. However, AI applications operate differently—they learn from data, goal-oriented, and often function as "black boxes" with complex decision-making processes. This fundamental difference opens up new avenues for attackers, such as adversarial prompts and data poisoning, which can undermine the integrity and reliability of AI systems.

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Development Phase

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Traditional Application Security

• Tools:
  • Software Composition Analysis (SCA): Identifies vulnerabilities in third-party libraries and dependencies.
  • Static Application Security Testing (SAST): Examines source code for insecure coding practices.
  • Fuzzing & Penetration Testing (PT): Performs proactive testing to uncover exploitable vulnerabilities.
• Risks/Controls:
  • Vulnerabilities: Code flaws that attackers can exploit.
  • Insecure Code: Non-compliance with security best practices.
  • Misconfigurations: Settings that expose systems to risks.

Traditional AppSec measures focus on code integrity, detecting known vulnerabilities, and preventing misconfigurations during development.

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AI Application Security

• Tools:
  • AI Supply Chain: Verify and maintain the integrity of AI components, such as pre-trained models and third-party datasets, to prevent introducing compromised elements
  • AI-SPM (AI Security Posture Management):  Continuously assess and improve AI security, ensuring models and data comply with policies and resist emerging threats.
  • AI Red Teaming: Tests AI models with unexpected or malicious inputs to identify AI-specific vulnerabilities.
• Risks/Controls:
  • Rogue Models: Unauthorized or malicious AI models that introduce security risks.
  • Data Poisoning: Injection of malicious data into training datasets to manipulate model behavior.
  • Data leakage: Unintended exposure of sensitive training or inference data.

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Why This Matters

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In AI development, ensuring the integrity of models and data is paramount. For example, an attacker could subtly alter your training data—a tactic known as data poisoning—leading your AI model to make incorrect predictions or classifications. Traditional AppSec tools might not catch this because they're not designed to monitor data quality.

Another concern is adversarial attacks, where attackers craft inputs that deceive AI models. These inputs might seem normal to humans but cause the AI to malfunction. Using techniques like AI Red Teaming allows you to examine how your LLM-based application responds to malicious inputs, helping to identify and fix weaknesses before attackers can exploit them.

There's also the risk of model theft. Attackers might try to replicate your AI model by sending numerous queries—a process known as model extraction. To protect your intellectual property, it's crucial to monitor access to your models and limit the information they reveal.

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Runtime Phase

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Traditional Application Security

• Tools:
  • Web Application Firewalls (WAF): Filters and monitors HTTP traffic to and from a web application.
  • API Security: Secures APIs from misuse and attacks.
  • Application Detection and Response (ADR): Detects and responds to threats within applications.
  • Dynamic Application Security Testing (DAST): Identifies vulnerabilities in running applications.
• Risks/Controls:
  • DDoS Attacks: Attempts to overwhelm a service to make it unavailable.
  • Data Leakage: Unauthorized disclosure of sensitive information.
  • Compromised Applications: Unauthorized access or control over applications.

Traditional AppSec in production focuses on defending against external attacks, protecting data, and ensuring application availability.

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AI Application Security

• Tools:
  • Tracing and Monitoring: Tracks AI model operations to detect anomalies and suspicious activities.
  • Guardrails and Controls: Establishes clear constraints for security risks and content moderation, preventing misuse of AI functionalities.
  • Data Integrity: Ensures data remains unaltered and trustworthy throughout processing.
• Risks/Controls:
  • Jailbreaking Attempts: Manipulating AI models to bypass restrictions and perform unintended actions.
  • Information Disclosure: Unintended exposure of sensitive data or internal logic.
  • Resource Hijacking: Unauthorized misuse of AI infrastructure for malicious activities.

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Why This Matters

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AI systems can be tricked into revealing sensitive information or behaving unexpectedly. For instance, in a jailbreaking attack, an attacker might craft inputs that bypass the model's safety features, causing it to produce disallowed content or expose confidential data.

To combat this, setting up guardrails is essential. These are policies and technical controls that restrict the AI's responses to safe and intended outputs. Additionally, continuous tracing and monitoring help detect unusual patterns that might indicate an ongoing attack or a vulnerability being exploited.

Another challenge is information disclosure. AI models might inadvertently reveal sensitive information they were trained on. Implementing anonymization techniques and carefully controlling training data can mitigate this risk, ensuring that outputs don't compromise privacy.

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Conclusion

Traditional AppSec tools weren't built with AI in mind, leading to several gaps:

• Dynamic Learning Processes: Agentic systems can learn and adapt, which static security measures can't adequately monitor.
• Data-Centric Threats: Attacks targeting the data used by AI systems, like poisoning or manipulation, aren't detected by traditional AppSec tools.
• Model Interpretability: The "black box" nature of AI makes it hard to understand when and how models are compromised.

These limitations mean that relying solely on traditional AppSec approaches leaves AI applications vulnerable.

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Pillar’s mission is to secure the new computing paradigm driven by AI and data. We help organizations secure and govern their AI systems by answering three critical questions:

1. Where and how is AI being used across your organization?
2. What risks and compliance gaps exist in your AI systems and use cases?
3. How can you mitigate these risks while maintaining AI innovation?

Want to learn more? Let's talk.