Autonomous AI red teaming platform that continuously tests AI agents, LLMs, and GenAI apps. 300+ attack techniques. OWASP & NIST mapped. Trusted b
Adversa AI is recognized for its forward-thinking approach in AI security, providing tailored solutions for AI agents, applications, and models. However, user discussions hint at a broader concern regarding AI security being somewhat experimental and still evolving. Pricing sentiments are not explicitly mentioned in the available input. Overall, Adversa AI seems to have a niche reputation as a pioneering player in its sector, albeit amidst a landscape of ongoing development and uncertainty in AI security.
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Adversa AI is recognized for its forward-thinking approach in AI security, providing tailored solutions for AI agents, applications, and models. However, user discussions hint at a broader concern regarding AI security being somewhat experimental and still evolving. Pricing sentiments are not explicitly mentioned in the available input. Overall, Adversa AI seems to have a niche reputation as a pioneering player in its sector, albeit amidst a landscape of ongoing development and uncertainty in AI security.
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computer & network security
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14
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Seed
Total Funding
$0.2M
Anyone else feel like AI security is being figured out in production right now?
I’ve been digging into AI security incident data from 2025 into this year, and it feels like something isn’t being talked about enough outside security circles. A lot of the issues aren’t advanced attacks. It’s the same pattern we’ve seen with new tech before. Things like prompt injection through external data, agents with too many permissions, or employees using AI tools the company doesn’t even know about. One stat I saw said enterprises are averaging 300+ unsanctioned AI apps, which is kind of wild. The incident data reflects that. Prompt injection is showing up in a large percentage of production deployments. There’s also been a noticeable increase in attacks exploiting basic gaps, partly because AI is making it easier for attackers to find weaknesses faster. Even credential leaks tied to AI usage have been increasing. What stood out to me isn’t just the attacks, it’s the gap underneath it. Only a small portion of companies actually have dedicated AI security teams. In many cases, AI security isn’t even owned by security teams. The tricky part is that traditional security knowledge only gets you part of the way. Some concepts carry over, like input validation or trust boundaries, but the details are different enough that your usual instincts don’t fully apply. Prompt injection isn’t the same as SQL injection. Agent permissions don’t behave like typical API auth. There are frameworks trying to catch up. OWASP now has lists for LLMs and agent-based systems. MITRE ATLAS maps AI-specific attack techniques. NIST has an AI risk framework. The guidance exists, but the number of people who can actually apply it feels limited. I’ve been trying to build that knowledge myself and found that more hands-on learning helps a lot more than just reading docs. Curious how others here are approaching this. If you’re building or working with AI systems, are you thinking about security upfront or mostly dealing with it after things are already live? Sources for those interested: AI Agent Security 2026 Report IBM 2026 X-Force Threat Index Adversa AI Security Incidents Report 2025 Acuvity State of AI Security 2025 OWASP Top 10 for LLM Applications OWASP Top 10 for Agentic AI MITRE ATLAS Framework submitted by /u/HonkaROO [link] [comments]
View originalTailored AI Security For AI Agents, Applications, Models, MCP and Beyond Before the buzz, beyond the horizon https://t.co/pKYPXxKunh
Tailored AI Security For AI Agents, Applications, Models, MCP and Beyond Before the buzz, beyond the horizon https://t.co/pKYPXxKunh
View originalAdversa AI uses a tiered pricing model. Visit their website for current pricing details.
Key features include: AI threat modelling, Continuous security assessment, Hardening remediation.
Adversa AI is commonly used for: Identifying vulnerabilities in AI models through prompt injection testing., Simulating adversarial attacks to evaluate model robustness., Continuous monitoring of AI systems for emerging threats., Automating red team exercises to ensure security posture adapts to changes., Assessing the impact of new tool integrations on existing AI security., Developing tailored threat models for specific AI applications..
Adversa AI integrates with: Slack for real-time alerts and updates., JIRA for tracking security issues and remediation tasks., GitHub for monitoring code changes and potential vulnerabilities., AWS for deploying security assessments in cloud environments., Azure for integrating with enterprise security frameworks., Google Cloud for managing AI model security in the cloud., Kubernetes for orchestrating security assessments in containerized environments., Splunk for analyzing security logs and incidents..
