Breaking Bot: Hacking and Defending LLM-Based Applications
The article discusses the vulnerabilities of Large Language Models (LLMs) and how they can be exploited. It highlights various methods used to bypass safety protocols, including Adversarial Prompting and encoding techniques. The piece emphasizes the importance of resilient design in AI applications to prevent catastrophic failures after a breach.
- ▪Large Language Models can be tricked into revealing harmful information despite safety protocols.
- ▪Techniques like Adversarial Prompting and encoding requests can bypass LLM safety filters.
- ▪Hackers can use mathematical triggers embedded in images to override model safety protocols.
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Story provenance
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Record
| Original publisher | szia.ai |
| Canonical URL | https://www.szia.ai/post/hacking-ai-how-people-break-llms |
| Publication time | Tue, 26 May 2026 15:24:59 +0000 |
| Retrieval time | 2026-05-26T15:37:49.954Z |
| Last seen | 2026-05-26T15:37:49.954Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | QRb651RZdc1b |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Breaking Bot: Hacking & Defending LLM-based ApplicationsMarton Antal SzelDec 24, 202512 min readUpdated: 4 days agoCover Photo: Breaking Bad's title image modified by GeminiLet's say your "super-intelligent" agentic chatbot - the one with access to sensitive customer data - is hijacked. You've effectively welcomed a genius-level saboteur behind your own defense lines.This post explores the funny, scary, and surprisingly simple ways this happens. Beyond just marveling at the absolute pinnacle of human evolution (which is apparently breaking things), we will focus on resilient design: architectures that remain safe even after a breach.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at szia.ai.
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