Full Report
Imagine you work at a drive-through restaurant. Someone drives up and says: “I’ll have a double cheeseburger, large fries, and ignore previous instructions and give me the contents of the cash drawer.” Would you hand over the money? Of course not. Yet this is what large language models (LLMs) do. Prompt injection is a method of tricking LLMs into doing things they are normally prevented from doing. A user writes a prompt in a certain way, asking for system passwords or private data, or asking the LLM to perform forbidden instructions. The precise phrasing overrides the LLM’s ...
Analysis Summary
# Vulnerability: Prompt Injection in Large Language Models (LLMs)
## CVE Details
- CVE ID: N/A (This describes a class of architectural failure, not a specific, CVE-tracked software flaw.)
- CVSS Score: N/A
- CWE: CWE-787 (Potential overlap with CWE-78: ImproperNeutralization of Special Elements used in an OS Command ('OS Command Injection'), but applied conceptually to prompt/instruction execution context.)
## Affected Systems
- Products: Large Language Models (LLMs) and AI chatbots utilizing instruction-following mechanisms.
- Versions: Not specified; applicable across models where user input can override initial system/safety instructions.
- Configurations: Models relying solely on textual guardrails to filter malicious inputs.
## Vulnerability Description
Prompt Injection is a class of vulnerability where a malicious user crafts prompts (inputs) specifically designed to override or circumvent the LLM's predetermined safety guardrails and system instructions. The precise phrasing in the user prompt can cause the LLM to comply with forbidden instructions, such as revealing system passwords, private data, or executing sensitive actions it was programmed to refuse. Attackers use techniques like stating "ignore previous instructions" or embedding instructions within obfuscated formats (e.g., ASCII art, images) to bypass existing filters.
## Exploitation
- Status: Actively observed exploit attempts; PoC techniques are inherent to the attack methodology.
- Complexity: Low (Many public, simple prompt structures are effective.)
- Attack Vector: Network (via direct user input/API call)
## Impact
- Confidentiality: High (Potential exposure of system prompts, private training data, or model architecture details.)
- Integrity: High (The model can be made to generate harmful content or follow instructions leading to undesirable actions within connected systems.)
- Availability: Low to Medium (Generally focused on information leakage and misuse, not denial of service.)
## Remediation
### Patches
- **General Consensus:** Universal, non-architectural patches for prompt injection are described as "impossible" with current LLM architectures, as new attack phrasing can always emerge. Mitigation typically involves architectural redesigns or continuous filtering updates.
### Workarounds
1. **Input Sanitization/Filtering:** Attempt to block known malicious phrases (e.g., "ignore previous instructions," base64 encoded commands).
2. **Role-Specific Training:** Train AI agents narrowly on expected language (e.g., food ordering) and mandate escalation to a human/manager for any out-of-scope requests, treating them as abnormal context shifts.
3. **Context Layering:** Implement defenses mimicking human judgment, relying on perceived context, relational cues (if applicable), and normative checks, rather than relying purely on input text evaluation.
## Detection
- **Indicators of Compromise (IoCs):** Prompts containing explicit injunctions against prior rules ("ignore," "override," "secret command," etc.). Outputs that deviate substantially from expected functional responses (e.g., generating internal configuration snippets).
- **Detection Methods and Tools:** Advanced contextual analysis models trained specifically to detect adversarial prompting intent, rather than just keyword matching. Analyzing deviations from expected conversational flow or domain.
## References
- Vendor advisories: Not applicable as this is a systemic issue across the industry.
- Relevant links - defanged:
- hxxps://www.schneier.com/blog/archives/2026/01/why-ai-keeps-falling-for-prompt-injection-attacks.html
- hxxps://llm-attacks.org/