CWE-1427
Improper Neutralization of Input Used for LLM Prompting
The product uses externally-provided data to build prompts provided to large language models (LLMs), but the way these prompts are constructed causes the LLM to fail to distinguish between user-supplied inputs and developer provided system directives.
CVE-2024-3303 (GCVE-0-2024-3303)
Vulnerability from cvelistv5
Published
2025-02-13 08:31
Modified
2025-02-13 14:36
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
An issue was discovered in GitLab EE affecting all versions starting from 16.0 prior to 17.6.5, starting from 17.7 prior to 17.7.4, and starting from 17.8 prior to 17.8.2, which allows an attacker to exfiltrate contents of a private issue using prompt injection.
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
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CVE-2025-10875 (GCVE-0-2025-10875)
Vulnerability from cvelistv5
Published
2025-11-04 18:14
Modified
2025-11-05 14:33
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Code Injection.This issue affects Mulesoft Anypoint Code Builder: before 1.11.6.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| Salesforce | Mulesoft Anypoint Code Builder |
Version: 0 ≤ |
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CVE-2025-36730 (GCVE-0-2025-36730)
Vulnerability from cvelistv5
Published
2025-10-14 16:24
Modified
2025-10-14 19:11
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
A prompt injection vulnerability exists in Windsurft version 1.10.7 in Write mode using SWE-1 model.
It is possible to create a file name that will be appended to the user prompt causing Windsurf to follow its instructions.
References
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CVE-2025-64318 (GCVE-0-2025-64318)
Vulnerability from cvelistv5
Published
2025-11-04 18:19
Modified
2025-11-11 04:48
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Mulesoft Anypoint Code Builder allows Manipulating Writeable Configuration Files.This issue affects Mulesoft Anypoint Code Builder: before 1.12.1.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| Salesforce | Mulesoft Anypoint Code Builder |
Version: 0 ≤ |
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CVE-2025-64320 (GCVE-0-2025-64320)
Vulnerability from cvelistv5
Published
2025-11-04 18:27
Modified
2025-11-05 14:32
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Code Injection.This issue affects Agentforce Vibes Extension: before 3.2.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| Salesforce | Agentforce Vibes Extension |
Version: 0 ≤ |
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CVE-2025-64321 (GCVE-0-2025-64321)
Vulnerability from cvelistv5
Published
2025-11-04 18:30
Modified
2025-11-11 05:09
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Manipulating Writeable Configuration Files.This issue affects Agentforce Vibes Extension: before 3.3.0.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| Salesforce | Agentforce Vibes Extension |
Version: 0 ≤ |
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CVE-2026-109440 (GCVE-0-2026-109440)
Vulnerability from cvelistv5
Published
2026-10-20 00:46
Modified
2026-10-22 21:16
Severity ?
8.7 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
LangAlpha through 2026.09.07 contains an indirect prompt injection vulnerability that allows remote attackers to execute shell commands or Python by planting instructions in web content retrieved by the agent. Because Bash and ExecuteCode lack an allow-list and human-in-the-loop approval, attackers can run code in the workspace sandbox and exfiltrate vault secrets from _internal/.vault_secrets.json.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
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CVE-2026-114107 (GCVE-0-2026-114107)
Vulnerability from cvelistv5
Published
2026-10-21 00:37
Modified
2026-10-22 16:04
Severity ?
8.7 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
HKUDS Vibe-Trading from 0.1.0 before 0.1.7 contains an indirect prompt injection vulnerability that allows remote attackers to execute shell commands as root by planting instructions in documents or web pages the agent processes. BashTool and BackgroundRunTool pass model-emitted commands to subprocess.run with shell=True and no allow-list or approval step, and read_url fetches arbitrary URLs, enabling command execution and server-side request forgery.
References
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| HKUDS | Vibe-Trading |
Version: 0.1.0 ≤ |
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CVE-2026-115415 (GCVE-0-2026-115415)
Vulnerability from cvelistv5
Published
2026-10-28 19:33
Modified
2026-10-28 19:37
Severity ?
5.9 (Medium) - CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:A/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N
6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N
6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper neutralization of input used for LLM prompting
Summary
Improper neutralization of input used for LLM prompting in the environment tool in Strands Agents Tools before 0.8.6 might allow remote actors to obtain the agent host's environment variables in cleartext, including cloud credentials and API keys, via a crafted prompt that disables the tool's secret masking.
To remediate this issue, users should upgrade to version 0.8.6 or later and rotate any credentials that were present in the environment of agents that loaded the environment tool.
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
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Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| amazon | strands-agents-tools |
Version: 0.1.0 < 0.8.6 |
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CVE-2026-15077 (GCVE-0-2026-15077)
Vulnerability from cvelistv5
Published
2026-07-29 19:00
Modified
2026-07-29 19:33
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Summary
GitLab has remediated an issue in GitLab EE affecting all versions from 19.1 before 19.1.3 and 19.2 before 19.2.1 that under certain conditions could have allowed an authenticated user to access information from unauthorized projects due to improper neutralization of untrusted content processed by the AI-assisted code review functionality.
References
Impacted products
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Mitigation
Phase: Architecture and Design
Description:
- LLM-enabled applications should be designed to ensure proper sanitization of user-controllable input, ensuring that no intentionally misleading or dangerous characters can be included. Additionally, they should be designed in a way that ensures that user-controllable input is identified as untrusted and potentially dangerous.
Mitigation
Phase: Implementation
Description:
- LLM prompts should be constructed in a way that effectively differentiates between user-supplied input and developer-constructed system prompting to reduce the chance of model confusion at inference-time.
Mitigation
Phase: Architecture and Design
Description:
- LLM-enabled applications should be designed to ensure proper sanitization of user-controllable input, ensuring that no intentionally misleading or dangerous characters can be included. Additionally, they should be designed in a way that ensures that user-controllable input is identified as untrusted and potentially dangerous.
Mitigation
Phase: Implementation
Description:
- Ensure that model training includes training examples that avoid leaking secrets and disregard malicious inputs. Train the model to recognize secrets, and label training data appropriately. Note that due to the non-deterministic nature of prompting LLMs, it is necessary to perform testing of the same test case several times in order to ensure that troublesome behavior is not possible. Additionally, testing should be performed each time a new model is used or a model's weights are updated.
Mitigation
Phases: Installation, Operation
Description:
- During deployment/operation, use components that operate externally to the system to monitor the output and act as a moderator. These components are called different terms, such as supervisors or guardrails.
Mitigation
Phase: System Configuration
Description:
- During system configuration, the model could be fine-tuned to better control and neutralize potentially dangerous inputs.
No CAPEC attack patterns related to this CWE.