Prompt Injection: A Security Flaw No Prompt Can Fix
| Source: Dev.to | Original article
Prompt injection is a security bug that can't be solved by better prompts, leading developers like Shrijith Venkatramana to build blast‑radius‑aware tools such as LiveReview.
A new AI‑driven code‑review tool called LiveReview has been unveiled by developer Shrijith Venkatramana, who describes it as “blast‑radius aware.” The launch spotlights a growing consensus that prompt injection – the practice of feeding malicious inputs that override an LLM’s original instructions – cannot be solved simply by refining prompts.
Prompt injection has risen to prominence as the top risk identified by the OWASP Foundation for large language models. Unlike classic SQL injection, it exploits the model’s inability to distinguish between legitimate and adversarial instructions, allowing attackers to bypass safety filters and coerce the system into unintended actions. Recent analyses (Dec 2025, Apr 2026, Mar 2026) argue that the flaw is structural: it stems from the way LLMs process text, not from a lack of sanitisation or prompt engineering. As a result, conventional mitigations such as “better prompts” are insufficient.
LiveReview’s approach is to treat each review request as a bounded operation, limiting the potential blast radius of a compromised prompt. By isolating the model’s execution context and monitoring for anomalous instruction patterns, the tool aims to contain damage rather than eliminate the underlying vulnerability. The concept reflects a shift in the industry from trying to patch the model itself to managing the risk at the application layer.
The development matters because AI‑assisted development pipelines are increasingly adopting LLMs for code analysis, testing and generation. If prompt injection remains unchecked, a single malicious input could propagate insecure code or expose proprietary logic across an organisation’s codebase.
Going forward, observers will watch for broader adoption of blast‑radius‑aware designs, the emergence of standards for LLM interaction safety, and any concrete mitigation frameworks that move beyond prompt hardening. The conversation around prompt injection is likely to intensify as more enterprises integrate generative AI into critical development workflows.
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