As AI-powered applications become mainstream, security teams must adapt traditional AppSec strategies to address new AI-specific risks. AI models continuously evolve, creating a rapidly shifting and complex security landscape.. Below, we explore the key differences between AI Application Security (AI AppSec) and Traditional AppSec, along with static analysis security concerns specific to AI models.
1. Static Code vs. Binary File Security
Traditional AppSec
- Security assessments focus on static code analysis, identifying vulnerabilities in source files (e.g., Python, Java, JavaScript, C++).
- Code files can be scanned for injection attacks, misconfigurations, logic flaws, and dependency vulnerabilities.
AI AppSec
- AI models are typically stored as binary files, such as .pt, .onnx, or .gguf, meaning security tools must analyze compiled models (and might contain large nueral network), not source code.
- Instead of function calls and API vulnerabilities, AI security focuses on embedding of malicious code, model weights, and architecture.
- Example of static analysis checks for AI models:
✅ Malicious or unauthorized model modifications
✅ Embedding of unsafe operators in binary models
✅ Backdoors hidden in the model
2. Source Code vs. AI Pipelines
Traditional AppSec
- Security reviews target application source code, APIs, and business logic before deployment.
- CI/CD pipelines include security testing such as SAST (Static Analysis Security Testing) and DAST (Dynamic Analysis Security Testing).
AI AppSec
- AI models rely on complex pipelines, including data preprocessing, training in Jupiter Noetbook, validation, and inference, each introducing unique risks.
- Static analysis must focus on:
✅ Dataset validation – Ensuring datasets are clean and not manipulated.
✅ Model integrity checks – Confirming models are not tampered with before deployment.
✅ Dependency analysis – AI frameworks often use extensive third-party dependencies, which must be validated.
3. Graph Complexity in Security Analysis
Traditional AppSec
- Security tools analyze abstract syntax trees (ASTs) and control-flow graphs (CFGs) to detect code vulnerabilities.
- These graphs are relatively straightforward, tracking function calls, loops, and logic branches.
AI AppSec
- AI security involves graph analysis of neural networks, which is vastly more complex than traditional CFGs.
- Instead of tracking function execution, AI security must analyze:
✅ Unintended shortcuts in weight distributions that could allow adversarial exploits
✅ Architectural backdoors where specific input triggers unauthorized outputs
4. Attack Surfaces
Traditional AppSec
- Focuses on web applications, APIs, databases, and network security.
- Key threats include XSS, SQL Injection, CSRF, privilege escalation, and authentication bypasses.
AI AppSec
- AI introduces new attack vectors beyond traditional app security, such as:
✅ Data poisoning – Attackers inject malicious data during training to alter AI behavior.
✅ Model extraction – Attackers query the AI model repeatedly to reconstruct its logic.
✅ Adversarial inputs – Specially crafted inputs designed to manipulate AI outputs.
✅ Prompt injection (LLMs) – Manipulating AI-generated responses through maliciously crafted inputs.
5. Testing Scope in Static Analysis
Unlike traditional security scans, which look for SQL injections, authentication flaws, and hardcoded secrets, AI static analysis focuses on:
✅ Unsafe Operators in AI Models – Detecting operators capable of file I/O or remote execution.
✅ Model Supply Chain Security – Ensuring models come from trusted sources and are not tampered with.
✅ AI Model Metadata Integrity – Verifying that the model version, training data references, and optimization settings match expected values.
✅ Bias and Fairness Checks (Static Level) – Analyzing initial training datasets for unintended biases before deployment.
6. Compliance & Ethical Risks
Traditional AppSec
- Compliance focuses on CWV, and OWASP best practices for security and encryption.
AI AppSec
- AI applications must comply with AI-specific regulations, such as:
✅ EU AI Act, NIST RMF, MITRE ATLAS and OWASP framework– Requires transparency, explainability, and risk management for AI models.
✅ Bias & Fairness Testing – AI models must undergo fairness audits to prevent discriminatory behavior.
✅ Explainability Requirements – AI decisions should be interpretable and auditable by regulators.
7. Security Responsibilities: Developers vs. AI Engineers
Traditional AppSec
- Security is managed by application developers and security teams.
- Security testing integrates into CI/CD pipelines and is automated.
AI AppSec
- AI security responsibilities are split among:
✅ Data Scientists – Ensuring clean, unbiased, and secure training data.
✅ ML Engineers – Managing AI model security, adversarial testing, and version control.
✅ Security Teams (AppSec) – Monitoring AI model risks, ensuring compliance, and protecting AI endpoints.
8. Speed of Model Evolution & Security Challenges
Traditional AppSec
- Software security is typically structured with predictable release cycles (e.g., quarterly updates, patching schedules).
- Security teams can plan audits before major releases.
AI AppSec
- Model fine-tuning and retraining are very frequent. For example, The DeepSeek R1 model has spawned over 550 variations on Hugging Face less than a week after it was published, highlighting how fast AI models are forked, fine-tuned, and deployed.
- This rapid evolution means:
✅ Security teams must continuously scan models, not just during scheduled audits.
✅ Automated AI security tools must adapt to custom model tweaks in real time.
Final Thoughts: Adapting Security for AI-Powered Applications
AI security introduces new challenges that traditional AppSec strategies are not equipped to handle. The shift from static code analysis to binary model security, the complexity of neural network graphs, and the rapid evolution of AI models require a fundamentally different security approach. Organizations must integrate AI-specific static analysis, supply chain validation, and adversarial testing into their workflows. Security teams must also adapt to new compliance frameworks, emerging attack vectors, and decentralized AI model development to keep AI-powered applications secure.
