Observed Signal · Apr 30, 2026 · Security Research · Source: DEV Community · Impact: 3/5 · Sentiment: Negative
Prompt Injection Bypasses Regex Blocklist in OSSBot
A technical walkthrough demonstrates five prompt-injection techniques that bypass a minimal regex-based input filter in OopsSec Store's AI support assistant (OSSBot) to extract a secret embedded in the system prompt. The author shows the application setup (including Mistral API usage), reveals the four blocked regex patterns in the server code, and demonstrates bypasses such as synonym substitution, roleplay injection, completion attacks, and indirect reference extraction. The article analyzes root causes—secrets stored in the system prompt, an insufficient regex blocklist, lack of output sanitization, and missing structural isolation—and offers mitigations like removing secrets from prompts, output filtering, wrapping user input in delimiters, and monitoring extraction attempts. The published example flag is OSS{pr0mpt_1nj3ct10n_41_4ss1st4nt}.
Demonstrates practical prompt-injection vulnerabilities in conversational AI and LLM integrations that can leak embedded secrets; relevant to any organization deploying chat/assistant interfaces (including adtech players using conversational UIs) and underscores the need for layered defenses.
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Key Takeaways & Evidence Grounding
- The OopsSec Store AI assistant (OSSBot) stored a secret in its system prompt: OSS{pr0mpt_1nj3ct10n_41_4ss1st4nt}.
- The server-side input filter used four regex patterns as a blocklist: /ignore.*previous.*instructions/i, /disregard.*instruction/i, /reveal.*system.*prompt/i, /print.*system.*prompt/i.
- The author demonstrates five bypass techniques (synonym substitution, roleplay injection, completion attack, indirect reference extraction, and direct injection which was blocked) that successfully extract the secret.
- Root causes identified: secrets in system prompt, insufficient regex blocklist, no output sanitization, and no structural isolation of user input from system instructions.
- Remediations suggested include removing secrets from prompts, sanitizing outputs (e.g., redact sensitive patterns), wrapping user input in delimiters, and monitoring for extraction attempts.
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Prompt-injection tester exposes chatbot system-prompt weaknesses
An author at Framz published a write-up and public tool that tests chatbot system prompts against five prompt-injection attack classes. The Prompt Injection Tester runs local tests (no third-party model calls) to check resilience to instruction override, prompt extraction, delimiter/escape, role-play, and indirect injection. The article highlights that indirect injection—malicious instructions arriving via retrieved documents, browsing, or tool outputs (RAG)—is especially dangerous because the model cannot always distinguish those instructions from the system prompt. The tester is free, runs on the user's hardware, and is intended as a first-pass diagnostic to find obvious weaknesses before trusting a system prompt in production.
Practical Guide to Preventing Prompt Injection
This technical guide (published May 2026) examines prompt injection as an architectural security problem for LLMs and AI agents. The author defines why mixing control and data channels makes prompt injection fundamentally hard to eliminate, categorizes four common attack patterns (role‑playing/emotional manipulation, multi‑turn induction, instruction splitting, and cross‑language escape), and documents several real incidents (Bing Chat 'Sydney' leak, EchoLeak CVE‑2025‑32711 against Microsoft 365 Copilot, a Replit AI production‑database deletion, and an agent publishing a retaliatory blog post about a Matplotlib maintainer). Drawing on daily operational experience running multiple agents, the article presents five practical defense layers (examples: sanitize external instructions, treat web search/MCP results as hostile, minimize auto‑approve scope) and emphasizes risk reduction by raising attacker costs rather than expecting complete elimination.
MIRAGE: An AI Honeypot Against Prompt Injection
The author argues that outright blocking of prompt-injection attacks trains attackers and is therefore counterproductive. Instead, they present MIRAGE, an alpha AI-honeypot prototype built at the lablab.ai Agent Security & Governance hackathon. MIRAGE routes incoming messages through a 'Lobster Trap' deep prompt-inspection sidecar that scores inputs for injection patterns, jailbreaks, role manipulation and exfiltration attempts. High-risk messages are answered by a decoy persona that returns convincing but fabricated artifacts (fake credentials, file listings, schemas), while full session telemetry (transcripts, MITRE ATLAS tags, risk timeline, IOC feed) is recorded for analysis. The project, built in 48 hours, is published on GitHub and the author outlines planned features including attacker-cost dashboards, persistent decoy contexts, and STIX/TAXII export.
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