AI Vulnerability Database (@AvidMldb) header
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Open Source community researching AI Vulnerabilities. Report an AI Vuln: https://nitter.cf/t.co/2sSxAZRcQo… Join us on discord: https://nitter.cf/t.co/gCtRKg1Z4J

Joined September 2022
Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)Photo by AI Vulnerability Database (@AvidMldb)
The power to uncover harmful flaws in AI shouldn't rest solely with experts. But how can everyday users contribute to responsible AI development?
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Join Michelle Lam, Christina Pan, Carol Anderson, Nathan Butters, and @Borhane_B_H to explore IndieLabel, an innovative tool that puts algorithmic audits in the hands of everyday users. Come discuss the power of public participation in shaping the future of AI.
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AI Vulnerability Database retweeted
💥 Secure your LLM systems with Giskard's LLM #RedTeaming! 🏴‍☠️ Are you ensuring the safety of your LLM apps? Our new service will help you detect safety & security breaches in your LLM-based applications. Why Red Team LLMs? LLM Red Teaming is crucial for detecting and addressing vulnerabilities in LLMs, putting the security and reputation of your organization and customers first. Our LLM Red Teaming service identifies potential threats and weaknesses in your LLM apps. By developing a holistic threat model with real attack scenarios ⚡, we provide actionable insights to strengthen your AI systems against critical LLM risks, such as misinformation and data leaks. We adhere to top-tier frameworks like #MITREATLAS, @owasp, @AvidMldb, and @NIST, ensuring our practices meet global AI security protocols. #LLMSecurity #LLMs #AISafety #Cybersecurity
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AI Vulnerability Database retweeted
new vuln tracked: invisible text🫥can be used for prompt injection, exploiting the Unicode TAG block (UE0000-UE007F) > garak now supports probing for this fresh vulnerability, in goodside.Tag (including in default scans) happy hunting! nitter.cf/goodside/status/174551…
PoC: LLM prompt injection via invisible instructions in pasted text
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AI Vulnerability Database retweeted
Incredibly excited to talk to Congressional Staffers about pressing issues in AI Vulnerability with my fellow panelists next week. If you’re in DC, please say hi!
Replying to @AvidMldb
At this event, hands-on hacking will be followed by a panel discussion, and networking sessions. Our expert panel will feature @evijit (moderator), @ErickGalinkin @BadassDoGooder @Obnoxious_Wolf @nathanvan and @moo_hax! 3/
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📢 In summer last year, we were community partners of the largest ever Generative AI Red Teaming event at DEF CON 31. We are glad to announce that we'll be collaborating with @aivillage_dc (AIV) again by taking AI red teaming to the US Capitol next week! 1/
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At this event, hands-on hacking will be followed by a panel discussion, and networking sessions. Our expert panel will feature @evijit (moderator), @ErickGalinkin @BadassDoGooder @Obnoxious_Wolf @nathanvan and @moo_hax! 3/
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We're looking forward to next week! Meanwhile, check out more about HotH here: hackersonthehill.org/ /end
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✍ AVID: 2023 in review As AI became a household topic in 2023, the need for rigorous approaches to managing the risks of this emergent technology became obvious to the general public, companies, and governments around the world. 1/
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We couldn't have done without our community and network of collaborators spanning the globe. To all our friends, thank you for your support! 4/
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📢 We are glad to announce BiasAware, a powerful tool for detecting bias in datasets!! 1/🧵
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Kudos to @fre7am and Sudipta Ghosh for leading the project! We also thank AVID leads Carol Anderson @J_Novikova_NLP @sbmisi for their mentorship and support. 5/
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Check out the blog post for more details: avidml.org/blog/biasaware-1 And check out BiasAware in the Hugging Face space: huggingface.co/spaces/avid-m… /end
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