OWASP Releases Gen AI Security Solutions Cheat Sheet

𝐋𝐋𝐌 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐡𝐫𝐞𝐚𝐭𝐬: 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐚𝐰𝐚𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐏𝐫𝐨𝐦𝐩𝐭 𝐈𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧, 𝐉𝐚𝐢𝐥𝐛𝐫𝐞𝐚𝐤𝐢𝐧𝐠 & 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥 𝐏𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠 𝐀𝐭𝐭𝐚𝐜𝐤𝐬❓ For architects and managers deploying AI: If you think prompt injection is just "users typing weird stuff," you should absolutely continue reading. Prompt injection is a vulnerability in the conversation itself. An attacker injects a hidden command into a normal query, & the AI obediently follows it. We primarily find 2 main categories in Prompt injection attacks: ✅𝐃𝐢𝐫𝐞𝐜𝐭 𝐏𝐫𝐨𝐦𝐩𝐭 𝐈𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧 (𝐃𝐏𝐈): An attacker adds a malicious command to the user’s prompt. ✅ 𝐈𝐧𝐝𝐢𝐫𝐞𝐜𝐭 𝐩𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧 (𝐈𝐏𝐈): Malicious instructions are embedded 𝑖𝑛 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 (emails, images, webpage, vector db) The implication is stark: 𝒚𝒐𝒖𝒓 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕, 𝑳𝑳𝑴 𝒐𝒓 𝑪𝒐-𝒑𝒊𝒍𝒐𝒕 𝒄𝒐𝒖𝒍𝒅 𝒃𝒆 𝒕𝒓𝒊𝒄𝒌𝒆𝒅 in potentially leaking data, overriding system prompts, or performing unauthorized actions. Many evaluation showed that 𝐃𝐏𝐈 𝐚𝐧𝐝 𝐈𝐏𝐈 𝐀𝐭𝐭𝐚𝐜𝐤𝐬' 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐑𝐚𝐭𝐞 𝐀𝐒𝐑 was 𝐨𝐧 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝟕𝟐.𝟔𝟖%. Models like 𝐆𝐏𝐓-𝟑.𝟓 𝐓𝐮𝐫𝐛𝐨 𝐚𝐧𝐝 𝐆𝐞𝐦𝐦𝐚𝟐-𝟐𝟕𝐁 are particularly vulnerable, with ASRs 𝐨𝐟 𝟗𝟖.𝟒𝟎% 𝐚𝐧𝐝 𝟗𝟔.𝟕𝟓%, respectively. The integrity of your AI's output is only as strong as its resilience to such manipulations. 𝐓𝐡𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬: 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐚𝐧'𝐭 𝐛𝐞 𝐚𝐧 𝐚𝐟𝐭𝐞𝐫𝐭𝐡𝐨𝐮𝐠𝐡𝐭. It must be designed from day one. I'll be sharing more on GenAI Security risks & defenses architecture. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐦𝐞 to build 𝐀𝐈 𝐭𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬. #AI #PromptSecurity #CyberSecurityAwareness #TechStrategy #RiskManagement #PromptInjection #JailbreakingLLM #AdversarialAIattacks #ModelPoisoning #GenAIsecurityRisks #LLM #LLMSecurity #AISecurity #AgentSecurity

🚀 How Orca is Leading the Charge in AI Security (AI-SPM)🚀 As organizations embrace AI at warp speed, the risks are real: prompt injection, data poisoning, model drift, exposure of sensitive training data, and unchecked access to AI services. That’s why AI Security Posture Management (AI-SPM) is emerging as an essential pillar in any modern security stack. At Orca, we’re not just talking about AI security — we’ve built it into the core of our platform. Here’s how: 👉 End-to-end coverage — From training and fine-tuning to production inference, Orca delivers visibility and security across the entire AI lifecycle. 🫶 Unified platform, no silos — Instead of adding yet another point solution, Orca extends its existing cloud security model to include AI, leveraging our agentless SideScanning™ architecture.' 🔒 Sensitive data & secrets detection — We detect exposure of keys, tokens, and sensitive training inputs embedded in models or code repositories, and alert on misconfigurations or overprivileged access. ➰ Continuous posture & threat monitoring — Orca continuously monitors AI deployments and flags anomalous behavior, drift, or attacks in real time. 🐳 AI-powered remediation & insights — Thanks to our Orca AI enhancements, users can ask in natural language, receive prioritized remediation steps, and accelerate time to resolution. The bottom line: as AI becomes mission critical, security can’t be an afterthought. With Orca’s built-in AI-SPM, organizations can accelerate innovation and maintain confidence in the integrity, confidentiality, and reliability of their AI models.

More good stuff from the first chapter of the book I am reading. In case you are wondering my bias is towards AI security, so please don’t be disappointed that I am only focused on the security stuff from the Chapter. I think you might still want to pay attention if you are adopting agentic AI in your workflows: From: AI Agents in Practice: Design, implement, and scale autonomous AI systems for production By Valentina Alto (Author) Data Grounding & Hallucination Control · Retrieval-augmented generation (RAG) is introduced as a way to connect LLMs to external, business-specific datasets to reduce hallucinations. · Security concern: If the retrieval layer or vector database is compromised, attackers can poison the knowledge base and manipulate AI outputs. · Security around the use of RAG: It is important that we use trusted, validated data sources and monitor retrieval pipelines. We should apply data integrity checks to prevent adversarial attacks that can introduce false or misleading information into responses.   Beyond ensuring data integrity, we need to adopt layered defenses around the RAG pipeline. This includes implementing role-based access controls for who can modify or upload data into the vector database, encrypting embeddings both at rest and in transit, and maintaining version control with rollback options in case corruption is detected. Another vital requirement for RAG protection is continuous monitoring through anomaly detection, which can flag suspicious query patterns or unexpected retrieval results that may indicate poisoning attempts. These measures help ensure that the retrieval layer itself does not become the weakest link, preserving the trustworthiness of the AI system’s outputs. #AISecurity #AIAttacks #LLMSecurity #AgenticAI #AITrust #AICompliance #RiskManagement #DataIntegrity #CyberSecurity  

It's week 3 of #CybersecurityAwarenessMonth and here's a few more ideas to help us all in #BuildingACyberStrongAmerica I would be remiss if I didn't mention Artificial Intelligence (AI) at least once this month. While AI technologies continue to change rapidly, it's even more important that we as security professionals find ways to enable AI securely without slowing down innovation. Note: Co-Pilot was used to take my very lengthy content and summarize it down to the key points below. This is also not an exhaustive list but could serve as a great starting point or validation that you're not missing something in your AI journey. Building Responsible AI: Key Steps for Organizations • Set the Standard: Get executive support and form an AI governance committee that brings together legal, compliance, tech, and business leaders. • Start Small: Launch a pilot AI program in one department to test your processes before scaling up. • Use Proven Frameworks: Lean on established guides like the NIST AI Risk Management Framework for best practices. • Train Your Team: Foster a culture of ethical AI use—keep staff educated and encourage them to voice concerns safely. • Protect Privacy: Set clear ethical standards for data use, obtain consent, and anonymize sensitive info to build trust. • Secure Every Step: Integrate security checks and secure coding throughout your AI development process. • Watch Third-Party Risks: Assess vendors for how they use AI and protect your data before you partner up. • Assess & Act: Regularly review and address risks tied to your AI systems—don’t wait for problems to surface. • Stay Updated: Work with legal experts to keep pace with changing AI regulations and update your policies accordingly. AI success starts with governance, ethics, privacy, and ongoing vigilance.  Be sure to check out the additional resources CISA has to offer on this topic at https://www.cisa.gov/ai #SecurityFirst #AIGovernance #AICompliance #ResponsibleAI

AI Agents are scaling fast but security isn’t keeping up. The digital landscape is moving rapidly: autonomous AI agents are no longer just experiments, they are running in production, making decisions, accessing sensitive data, and interacting with other systems. Yet the security and governance layers are often missing. Without them, risks multiply: Data leaks: Sensitive info can escape via prompts, memory replay, or embedding vectors. Tool/API misuse: Agents might call the wrong services, execute harmful commands, or escalate privileges unintentionally. Autonomy drift: Agents can act outside intended goals or manipulate other agents. No auditability: Hard to trace decisions or reasoning steps if something goes wrong. We’re seeing these issues in real-world deployments where teams are struggling to scale AI agents safely while keeping them compliant and trustworthy. At tupl, we’re building a runtime governance and observability platform for AI agents: Grounding policies: Define safe zones and allowed actions for each agent. Semantic observability: Inspect reasoning traces, tool calls, and inter-agent communication. Trust & control: Identity, lineage, and dynamic revocation for agents in production. Adaptive governance: Policies that evolve as agents learn and operate in the wild. Security is no longer optional for autonomous AI - it’s foundational. We’re partnering with companies to co-build custom security layers for their AI agent workflows, so they can scale safely, maintain compliance, and trust every agent in production. DM me if your team is deploying or scaling AI agents. Let’s explore building a secure and compliant agent ecosystem together. #AI #ArtificialIntelligence #MachineLearning #AIAgents #MultiAgentSystems #AISecurity #AIGovernance #CyberSecurity #DataPrivacy #TrustAndSafety

My Saturday morning started with a meetup on Agentic AI security. here is what I walked away feeling.... During the Q&A, I asked one of the AI researchers if he ever built enterprise software ? With a smile.. he said no he had just come straight out of college. And there he was, confidently suggesting solutions for massive enterprises. At that point, I didn’t need to ask any more questions… the answer had already found me. Generative AI / Agentic AI .. is still software. Data needs protection. Models need protection. APIs need protection. The basics like identity, access, encryption, and monitoring stay the same. The difference comes when model behavior is involved. Outputs are unpredictable. Data can leak. Agents can act on their own. The focus shifts from locking doors to watching behavior. AI security can be seen in three parts. Input security checks what goes in. Output security controls what comes out. Operational security manages access, logging, versions, and costs. Most of AI security is not new. A large part is traditional application security. Another part is data governance. Only a small piece is new guardrails for model behavior. AI security is not mystical. It is software security plus data governance plus a little extra for model behavior. The real problem starts when we try to make it sound bigger than it is. #AISecurity #CyberSecurity #GenerativeAI #EnterpriseSoftware #DataGovernance #ApplicationSecurity #AITrust #ResponsibleAI #AICompliance #AIAgents #AICommunity

Episode 4: Confronting the Challenge of Data and Model Poisoning in AI Systems In the realm of AI systems, the looming threat of data and model poisoning poses a significant risk to their integrity. Unlike traditional cybersecurity threats that involve injecting malicious code, in AI, the danger lies in the manipulation of the data or the model itself. Adversaries can subtly corrupt training datasets or tweak model parameters to influence outcomes, introduce bias, or diminish performance without detection until it's too late. At Resilience, we perceive poisoning not simply as a technical vulnerability but as an attack on the reliability of AI decision-making. The repercussions span from skewed analytics to the jeopardization of national-scale systems that heavily rely on AI for governance, defense, or vital infrastructure. To combat these emerging threats effectively, organizations need to: - Validate and cleanse training data: Incorporate data provenance checks and anomaly detection mechanisms for incoming datasets. - Monitor for model drift and tampering: Continuously assess output behaviors against established integrity benchmarks. - Utilize distinct training environments: Segregate, evaluate, and authenticate models pre-deployment. - Embrace threat-informed AI development: Handle AI pipelines akin to critical supply chains, with robust logging, audits, and access controls. Safeguarding AI entails safeguarding its core elements — the data and the underlying logic it acquires. A compromised dataset today could lead to compromised decisions in the future. Join us in our OWASP Top 10 for LLMs series as we delve into how governance, vigilance, and secure engineering practices can fortify the trajectory of trustworthy AI. #AISecurity #RTG #Resilienceza #Resilience #OWASP #LLMSecurity Toni Bowker Hennie Marais Jason Duncan

🚨 VirusTotal integrates security scanning directly into Hugging Face's AI model platform, addressing the growing threat landscape where compromised AI models pose supply chain risks to downstream applications. 🔍 The collaboration tackles emerging AI security challenges including tampered model files, unsafe dependencies, data poisoning, and hidden backdoors that can silently compromise applications. VirusTotal's analysis now appears alongside model downloads, providing real-time security context before users access potentially risky files. 🛠️ VirusTotal is deploying specialized AI security tools including Code Insight for LLM-powered code analysis, plus support for picklescan, safepickle, and ModelScan to detect unsafe deserialization patterns in model formats. These tools specifically target the unique attack vectors present in machine learning workflows. 📊 This integration represents a critical evolution in AI supply chain security, where traditional malware detection meets the specialized threats of model tampering and dataset poisoning. The partnership enables real-time threat intelligence sharing across the AI development ecosystem. The integration demonstrates how traditional security vendors must adapt their detection capabilities for AI-specific attack vectors and supply chain vulnerabilities. #ThreatIntelligence #AISecuritya #SupplyChainSecurity #InfoSec #MachineLearning #CyberThreat #SecurityOperations #DevSecOps #DataProtection #TechSecurity source: https://lnkd.in/dnn7cNPH

Intruder’s recent exploration into AI-powered vulnerability checks offers valuable insights into the evolving landscape of security automation. Early experiments with mainstream chatbots using one-shot prompts revealed significant shortcomings, erratic syntax, missed features, and unreliable results, highlighting that generic AI chat isn’t enough for the complexities of engineering-grade security tasks. The breakthrough emerged with agent-based AI systems that research, reference, and follow explicit instructions. Curated prompts and guidance transformed these tools into powerful allies, significantly improving the quality and coverage of custom vulnerability checks. This partnership between skilled engineers and intelligent agents enhances productivity without sacrificing accuracy, tackling surfaces like exposed admin portals and insecure databases more effectively. However, challenges remain. Issues such as overly broad matchers and truncated outputs underscore the importance of human oversight. AI can accelerate detection but still relies on domain expertise for validation and fine-tuning. This hybrid approach, where engineers define logic and AI executes at scale, promises a future where security teams can rapidly adapt to emerging threats while maintaining control. The broader takeaway? While AI-driven automation continues to advance, it’s no substitute for human judgment. False positives and missed threats still demand diligent review and expertise. The industry’s journey toward trustworthy, scalable vulnerability management will hinge on collaborative AI-human workflows that leverage intelligent automation without losing sight of accuracy and context. Intruder’s findings serve as a reminder: automation is a powerful enabler, but responsible security requires continuous oversight, iterative improvement, and strategic collaboration with AI tools. As we look ahead, the question remains, how can we best harness these innovations to strengthen our defenses today and tomorrow? Don't just take our word for it, read the full story here: https://lnkd.in/e87jHWid #SECURITYOPERATIONS #BLUETEAM #CYBERSECURITY #SOC #DIRECTOROFAI