AI Security

As organizations increasingly integrate Artificial Intelligence (AI) into their operations, addressing associated risks, particularly in the realm of cybersecurity, is paramount. The widespread adoption of AI introduces a massive new attack surface that security leaders are actively grappling with, with risks present throughout the entire AI development lifecycle. AI systems face diverse vulnerabilities and threats, including external threats like AI-enabled malware and internal threats arising during the AI adoption process. Further emerging risks include data exposure and unauthorized AI use within cloud environments. Specific AI-related attack vectors encompass prompt injection attacks, data poisoning, data extraction attacks, and adversarial attacks, where malicious actors manipulate AI models with crafted inputs to cause incorrect predictions or unintended actions. The theft of trained AI models is also a significant concern, alongside algorithmic jailbreaking of Large Language Models (LLMs), which can bypass model protections to exfiltrate sensitive data and disrupt AI services. The financial consequences of these vulnerabilities are severe, with each AI-related data breach costing companies an average of $4.35 million, largely attributed to lost business and recovery efforts.

Beyond cybersecurity, other significant challenges associated with AI deployment include AI inaccuracy and the potential for hallucinations, which can be managed through effective prompt engineering. Concerns about personal privacy, biased outputs, equity, and fairness are also prominent, as is the potential misuse of AI for creating harmful content or enabling fraud. Intellectual property infringement presents another notable risk. A critical challenge, especially for tasks requiring trust, is the lack of explainability in many LLMs, often described as "black boxes" that do not reveal their reasoning or data sources. Despite progress, challenges in explainability and accuracy persist, yet leaders acknowledge that new capabilities bring new risks that must be managed rather than eliminated.

Addressing these multifaceted AI risks necessitates a proactive and comprehensive approach. Key mitigation strategies include establishing robust governance structures that align senior leadership, define AI's value, and outline risk mitigation strategies, possibly through the appointment of a GenAI value and risk leader. Implementing strong security controls and standards throughout the AI lifecycle is crucial, involving comprehensive vulnerability assessments, adoption of standards like the NIST AI Risk Management Framework and MITRE ATLAS matrix, and real-time monitoring. Furthermore, organizations must prioritize training and workforce readiness through reskilling programs, and implement rigorous testing and review of AI outputs to ensure quality and prevent unintentional consequences. Planning for agentic AI by developing strategic roadmaps and starting with low-risk use cases under human oversight is also vital. Importantly, AI plays a crucial dual role in cybersecurity by offering powerful defense capabilities. It is leveraged for threat detection and response, automating incident response, performing vulnerability scanning, and detecting s ocial engineering attempts. Generative AI (GenAI) specifically boosts software security by triaging cyberthreat alerts, reducing false positives, and automating security protocols, allowing security teams to focus on critical issues. Tools that combine AI-powered security analysis with code improvement suggestions are also emerging to promote secure development practices. Investing in these security measures is significant, with organizations spending an average of $1.2 million annually on securing AI infrastructures, emphasizing the need for collaboration between security teams and developers to effectively address emerging risks and build trust in AI systems for successful adoption.

Explore a comprehensive breakdown of the most critical AI security threats. From prompt injection to data poisoning, understand the vulnerabilities facing modern AI systems.

Discover actionable fixes and best practices to fortify your AI defenses.

Prompt Injection Attacks (Critical)

Issue: Malicious inputs designed to manipulate AI models into performing unintended actions or revealing sensitive information.

Fixes:

• Implement rigorous input validation and sanitization


• Use context-aware filtering to detect malicious prompts


• Deploy prompt injection detection algorithms


• Establish strict guidelines for acceptable inputs


• Implement rate limiting and user authentication

Training Data Poisoning (Critical)

Issue: Malicious alteration of training datasets causing AI models to learn dangerous behaviors.

Fixes:

• Implement strong data validation during training


• Use data provenance tracking and verification


• Deploy anomaly detection in training datasets


• Establish secure data pipelines with integrity checks


• Regular auditing of training data sources

Insecure Output Handling (High)

Issue: AI-generated content executed without proper validation, leading to code injection or XSS attacks.

Fixes:

• Sanitize all AI outputs before execution


• Implement output validation frameworks


• Use sandboxed environments for AI-generated code


• Apply content security policies


• Regular security testing of output handling

Model Theft and Extraction (High)

Issue: Unauthorized access to proprietary AI models through API queries or reverse engineering.

Fixes:

• Implement robust API authentication and authorization


• Use query rate limiting and monitoring


• Deploy model watermarking techniques


• Implement differential privacy measures


• Use secure model serving infrastructure

Denial of Service (DoS) Attacks (High)

Issue: Resource exhaustion through computationally expensive queries designed to overwhelm AI systems.

Fixes:

• Implement query complexity analysis


• Set resource usage limits per user/session


• Use load balancing and auto-scaling


• Deploy DDoS protection services


• Monitor system performance and set alerts

Supply Chain Vulnerabilities (High)

Issue: Compromised AI models, libraries, or components from third-party sources.

Fixes:

• Verify integrity of all AI components and models


• Use trusted sources and repositories only


• Implement software composition analysis


• Regular security audits of dependencies


• Maintain an inventory of all AI components

Sensitive Information Disclosure (High)

Issue: AI models inadvertently revealing training data, personal information, or confidential data.

Fixes:

• Implement data anonymization and pseudonymization


• Use differential privacy techniques


• Regular privacy impact assessments


• Implement data retention and deletion policies


• Deploy membership inference attack detection

Adversarial Attacks (Medium)

Issue: Specially crafted inputs designed to fool AI models into making incorrect predictions.

Fixes:

• Implement adversarial training techniques


• Use input preprocessing and noise reduction


• Deploy ensemble methods for robustness


• Regular adversarial testing and validation


• Implement uncertainty quantification

Model Inversion and Membership Inference (Medium)

Issue: Attackers reconstructing training data or determining if specific data was used in training.

Fixes:

• Apply differential privacy during training


• Implement gradient noise injection


• Use federated learning approaches


• Regular privacy audits and assessments


• Implement access controls and monitoring

Insecure Plugin and Tool Integration (Medium)

Issue: Vulnerabilities in AI agent plugins and external tool integrations.

Fixes:

• Implement strict plugin validation and sandboxing


• Use least privilege principles for tool access


• Regular security audits of integrations


• Implement plugin permission systems


• Monitor and log all external tool interactions

Bias and Fairness Issues (Medium)

Issue: AI systems producing discriminatory or unfair outcomes due to biased training data or algorithms.

Fixes:

• Implement bias detection and monitoring tools


• Use diverse and representative training datasets


• Regular fairness audits and assessments


• Implement algorithmic accountability measures


• Establish bias remediation procedures

Unauthorized AI Model Access (Medium)

Issue: Insufficient access controls allowing unauthorized users to interact with AI systems.

Fixes:

• Implement multi-factor authentication


• Use role-based access control (RBAC)


• Regular access reviews and audits


• Implement session management controls


• Deploy privileged access management (PAM)

This section provides a detailed checklist of security issues inherent in AI-powered coding. From deep-seated model limitations to critical OWASP LLM risks, understand every facet of the threat landscape

Equip yourself with the knowledge to address these challenges and secure your AI development pipeline.

Code Generation Issues

• Vulnerable Code Pattern Reproduction: AI reproduces code patterns from training data without understanding security implications


• SQL Injection Vulnerabilities: AI generates code with SQL injection vulnerabilities


• Insecure Authentication Mechanisms: AI doesn't understand proper authentication or data flow logic


• Improper Authorization Controls: AI generates code with improper authentication/authorization


• Insecure File Handling: AI creates insecure file handling mechanisms


• Malicious Code Injection: AI can suggest code with malicious additions

Development Process Risks

• Reduced Security Reviews: Shorter development cycles mean less time for manual security reviews


• Skipped Threat Modeling: In the rush to ship features, threat modeling takes a backseat


• Silent Killer Vulnerabilities: AI-generated code creates vulnerabilities that function perfectly in testing but contain exploitable flaws


• Bypassed Security Tools: Vulnerabilities that bypass traditional security tools and survive CI/CD pipelines

AI Model Limitations

• Context Blindness: AI lacks awareness of the broader application security context


• Training on Legacy Code: Large portions of training data contain outdated security practices


• Incomplete Implementation: AI may provide incomplete security implementations


• Missing Security Best Practices: AI-generated code can miss critical security best practices

Data Exposure Risks

• Sensitive Code Snippets to External APIs: Tools may send sensitive snippets to external APIs, risking exposure of internal logic, secrets, or customer data


• Over-Permissioned AI Agents: AI-powered coding tools require broad permissions and could expose data if compromised


• Data Handling Policy Violations: Especially problematic in regulated industries where data handling policies are strict

Business Logic Flaws

• Poor Understanding of Business Logic: AI doesn't understand authentication or data flow logic, and business logic flaws creep into code


• Unvalidated Assumptions: More security gaps and unvalidated assumptions in rapid development


• Logical Errors in Generated Code: AI may create code that compiles but contains logical security flaws

OWASP LLM Top 10 Risks

• LLM01: Prompt Injection: Malicious inputs manipulating the AI coding assistant


• LLM02: Insecure Output Handling: Generated code executed without proper validation


• LLM03: Training Data Poisoning: AI trained on compromised or malicious code examples


• LLM04: Model Denial of Service: Resource exhaustion through complex code generation requests


• LLM05: Supply Chain Vulnerabilities: Compromised AI coding tools or models


• LLM06: Sensitive Information Disclosure: AI revealing sensitive information in generated code


• LLM07: Insecure Plugin Design: Vulnerable AI coding tool integrations


• LLM08: Excessive Agency: AI making unauthorized changes or decisions


• LLM09: Overreliance: Developers trusting AI-generated code without verification


• LLM10: Model Theft: Unauthorized access to proprietary AI coding models

Deployment & Infrastructure Risks

• Inadequate Input Validation: Missing validation of AI-generated code inputs


• Insufficient Rate Limiting: No controls on AI code generation requests


• Missing Dynamic Resource Allocation: Uncontrolled resource consumption


• Lack of HTTPS/SSL Implementation: Insecure deployment of AI-generated applications


• Missing DDoS Mitigation: No protection against AI-assisted attacks

Human Factor Issues

• Developer Security Training Gap: Many vibe developers lack proper security training


• Over-reliance on AI: Developers not reviewing or understanding generated code


• False Sense of Security: Assuming AI-generated code is automatically secure


• Lack of Code Review Processes: Skipping human review of AI-generated code

The critical flaw discovered in the Base44 AI-powered development platform serves as a concrete example of a vulnerability within a specific vibe coding platform and illustrates a real-world security breach. Base44 is described as a popular vibe-coding platform that allows developers and software teams to rapidly build and deploy applications using natural language descriptions instead of traditional programming code. The vulnerability was an authentication issue that gave unauthorized users open access to any private application hosted on Base44, including those with paid subscriptions ranging from individual developers to enterprise organizations. Cloud security firm Wiz discovered this flaw, noting that it was "especially scary" due to the low barrier to entry for exploitation, meaning attackers with minimal technical sophistication could systematically compromise multiple applications.

The issue stemmed from Base44 inadvertently leaving two supposedly hidden parts of its system—one for new user registration and another for one-time password (OTP) verification—open to public access without requiring any login or special authentication. Wiz researchers found that if an attacker discovered a Base44 app ID, which was easily discoverable due to being publicly accessible, they could input this ID into these exposed sign-up or verification tools. This allowed them to register a valid, verified account for an application they did not own, even for applications configured for single sign-on (SSO-only) access. Wiz's head of threat exposure, Gal Nagli, stated that the problem originated from a logic flaw in Base44's authentication workflow, where internal API documentation exposed all necessary parameters for registering within private applications.

Although Wix, which acquired Base44, stated they found no evidence of customer impact prior to Wiz's report, the vulnerability put potentially thousands of enterprise applications at risk, including company chatbots and those containing personally identifiable information (PII) and sensitive information. This incident highlights concerns about the relative lack of enterprise readiness of some vibe-coding platforms from a security and compliance perspective. Nagli emphasized that while vibe coding democratizes software development, its widespread popularity introduces new attack surfaces, underscoring that fundamental controls, including proper authentication and secure API design, are paramount. This case concretely illustrates how basic security lapses in rapidly adopted AI development tools can lead to significant real-world exposure and potential breaches.

There is an intersection of AI, particularly vibe coding and cybersecurity, highlighting both the immense productivity benefits and the emerging security challenges. Vibe coding, which involves using large language models (LLMs) for rapidly generating software with natural language descriptions, has proven to be a significant boon for developers and engineering teams, enabling them to write large parts of applications quickly and efficiently. For one-off scripts, it can provide a 10x productivity boost. However, this rapid adoption has also raised concerns about the relative lack of enterprise readiness of some vibe-coding platforms from a security and compliance standpoint.

A significant security challenge discussed is the tendency for developers to not scrutinize AI-generated code as closely as if they had written it themselves, leading to potential oversights. Furthermore, these tools often reach for dependencies, bringing in third-party code that may not be vetted, posing risks if malicious or deprecated libraries are introduced. While AI seems to be improving at preventing traditional vulnerabilities like SQL injection or cross-site scripting by remembering rules, it simultaneously foregrounds new types of vulnerabilities such as key management issues, authorization problems, and race conditions. The general rush to market with AI tools has often made security an afterthought, much like in the early days of cloud computing.

There is a new scenario where the most productive and skilled engineers become even more productive, while the least knowledgeable individuals, when using these tools, becomes perhaps the most destructive. This is because AI makes less experienced users good enough to be dangerous due to a lack of inherent guardrails. A major concern highlighted is the use of Model Context Protocol (MCP) servers: developers may bypass secure development lifecycles (SDLC) by running MCP servers locally, taking credentials home, and then trying to integrate unvetted code back into production environments. This practice can leave sensitive data and tokens exposed on local disks, ripe for exfiltration by attackers.

Despite the challenges, there is an ongoing debate about whether AI will eventually solve security. Some experts believe that AI will largely eliminate technical coding mistakes and that new, more secure languages will emerge. However, the consensus emphasizes that humans will likely always be in the loop due to the adversarial nature of cybersecurity and the human element of making judgment calls. The conversation highlights the continued importance of code review, robust SDLCs, linting, and scanning even when using AI for code generation. Tools like Socket are mentioned as helping by providing real-time information on third-party dependencies, ensuring developers are aware of security statuses that AI models, trained on older data, might not know. Ultimately, while AI democratizes development, it introduces new attack surfaces that necessitate fundamental controls, including proper authentication and secure API design.

There is a significant and escalating threat in the software supply chain: hackers are increasingly weaponizing open-source code and targeting developers directly. Open-source code, being publicly accessible, is easy for attackers to find and exploit vulnerabilities within, for that reason, it was reported a boom in malware targeting these public software repositories, such as npm and PyPI. In the second quarter of 2025 alone, 16,279 new pieces of malicious code were uncovered, bringing the total to over 845,000. The primary goal of these attacks is data exfiltration, aiming to quietly compromise developer environments from the inside out.

Unlike traditional phishing scams that target office workers, this malware is specifically engineered to go after developers and their sensitive information. Attackers hide malicious code within everyday software libraries used by developers to steal crucial data such as .git-credentials, AWS secrets, environment variables, and CI/CD tokens. This collection of credentials can then lead to unauthorized access to cloud accounts, APIs, databases, and internal systems, paving the way for broader compromise and exploitation. Specific examples cited include an April 2025 incident where developers were tricked by malware masquerading as the CryptoJS library, harvesting sensitive data like crypto wallet info and MongoDB connection strings. Furthermore, campaigns like Yeshen-Asia, attributed to a suspected Chinese threat actor, and a steady drip-feed of malware from the Lazarus Group (North Korea-backed) demonstrate the organized nature of these attacks, with malicious packages pushed through multiple author accounts and funneling data to command-and-control infrastructures.

This trend marks an escalating arms race where developers, rather than end-users, have become the front-line targets. The danger lies in these strikes quietly laying the groundwork for massive supply chain breaches and cloud takeovers. To mitigate these risks, developers are advised to use trusted packages, apply updates quickly, audit dependencies, and monitor for security advisories. The source emphasizes that the security of code, whether open or closed source, ultimately depends on how well it is maintained and audited.

The state of Model Context Protocol (MCP) servers presents a significant cybersecurity challenge, primarily due to a widespread lack of fundamental security controls. Researchers from Knostic identified nearly 2,000 MCP servers exposed to the internet that had no authentication or access controls whatsoever. This alarming discovery indicates that despite authentication being an optional feature within the MCP protocol, virtually no known users are implementing it, effectively granting any passing attacker full control of these servers. This issue is emblematic of a broader trend in AI's rapid adoption, where security is often an afterthought in the rush to market.

This lack of authentication allows exposed MCP servers to nakedly list their private parts — executable functions or tools — to anyone without requiring any form of credential. Knostic's research revealed instances where these servers exposed connectors to databases, cloud services management tools, corporate productivity applications, and even legal databases containing sensitive case information. Consequently, attackers could exploit these vulnerabilities to execute arbitrary commands, exfiltrate sensitive data such as files, credentials, and API keys, or even launch denial of wallet attacks by consuming victims' computational resources. The problem is compounded by developers frequently storing sensitive tokens for various accounts, like Gmail, in accessible files (e.g., MCP.json) on their systems, which become easy targets if the MCP server is compromised.

The insecure state of MCP servers is partly attributed to the design philosophy of much new AI technology, which aims to be functional out of the box, and super easy to use. This ease of use can draw in developers who haven't yet learned critical security practices, leading them to bypass secure development lifecycles (SDLCs). Instead of integrating new services securely, some developers are running MCP servers locally and taking sensitive credentials home, then attempting to reintegrate unvetted code and tokens into production environments. This practice creates significant risk by leaving valuable data vulnerable to exfiltration from local machines. While Anthropic, the protocol's inventor, has updated its specifications to offer more security guidelines, authentication remains optional, underscoring the ongoing need for fundamental controls like proper authentication as new AI-powered attack surfaces emerge.