Call Chains - AI Security Technique
AI Security TechniqueAdversaries may extract call chains from AI agent configurations, which can reveal potentially targets for remote code execution (RCE) or other vulnerabilities. Vulnerable call chains often connect user inputs or LLM outputs to an execution sink (e.g. exec, eval, os.popen). The vulnerabilities may be later exploited via LLM Prompt Injection. Adversaries may systematically identify potentia...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may extract call chains from AI agent configurations, which can reveal potentially targets for remote code execution (RCE) or other vulnerabilities. Vulnerable call chains often connect user inputs or LLM outputs to an execution sink (e.g. exec, eval, os.popen). The vulnerabilities may be later exploited via LLM Prompt Injection.
Adversaries may systematically identify potentially vulnerable call chains present in LLM frameworks, then scan for applications that are configured to use these call chains for targeting [1].
References
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0084.003
- Maturity
- demonstrated
- Priority score
- 90
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses0 ATLAS mitigation records
- Public examples1 linked case study records
- Research risks12 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
LLMSmith: RCE Vulnerabilities in LLM-Integrated Applications
Researchers identified 20 remote code execution (RCE) vulnerabilities across 11 different LLM frameworks. They discovered applications deployed on the public internet built using these LLM frameworks and demonstrated the RCE vulnerabilities could be exploited using prompt injection.
The 11 LLM frameworks the researchers evaluated were: LangChain, LlamaIndex, Pandas-ai, Langflow, Pandas-llm, Auto-GPT, Griptape, Lagent, MetaGPT, vanna, and langroid.
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.