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Now we have systems vulnerable to social engineering

Hacker News

Now we have systems vulnerable to social engineering

I wrote an AI-Agent Vulnerability and Risk Report. It’s a technical writeup covering the new attack surface AI-agents and LLMs introduce. The report is objective and focused on security, legal, and regulatory risks, not speculation. The report covers: The attack surface of AI-agents and LLMs. Exploitation pathways such as prompt injection, privilege misuse, and workflow manipulation. The legal and technical difference between prompt injection and SQL injection. Regulatory exposure (e.g. GDPR liability) when data leaks occur. Mitigation strategies to reduce risk, including backend immune layers. Full report: https://github.com/pablo-chacon/AI-Agent-Vulnerability-and-R... Would love to hear feedback, especially from those working in AI security, infrastructure, or compliance.

Visit productView on Hacker NewsAug 28, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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