CVE-2024-58340 — HIGH (CVSS 7.5) AI Security Vulnerability

CISO Take

Any LangChain-based application running MRKL agents on version 0.3.1 or earlier is vulnerable to a DoS attack delivered via prompt injection — no authentication required. An attacker who can influence LLM output (e.g., through user-supplied prompts in a downstream app) can stall your agent service with a single crafted string. Patch to LangChain >0.3.1 immediately; if you cannot patch today, wrap MRKLOutputParser calls with a timeout and sanitize LLM output before parsing.

Risk Assessment

CVSS 7.5 High with AV:N/AC:L/PR:N/UI:N is accurate for the worst case. Real-world exploitability requires the attacker to first achieve prompt injection against the target application — an increasingly realistic assumption for any public-facing LLM app. The exploit chain is two steps: (1) inject a crafted payload via user input, (2) the LLM reflects it and the app feeds it to the vulnerable parser. CPU exhaustion is the outcome, not code execution or data leakage, which limits blast radius. Organizations running MRKL/ReAct agent architectures at scale or in multi-tenant SaaS products face the highest exposure.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
langchain	pip	—	No patch
135.7K OpenSSF 6.5 2.6K dependents Pushed 7d ago 17% patched ~256d to patch Full package profile →

Do you use langchain? You're affected.

Severity & Risk

CVSS 3.1

7.5 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 24% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Moderate

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

Recommended Action

6 steps

PATCH

Upgrade langchain to the first version past 0.3.1 that includes the fixed regex; verify with pip show langchain.
WORKAROUND (if patch is not immediate): Wrap MRKLOutputParser.parse() calls with a signal-based or thread-based timeout (e.g., 2–5 seconds); raise a parsing error and abort on timeout.
INPUT HYGIENE

Truncate LLM output to a reasonable maximum length (e.g., 4 KB) before passing to the parser; reject outputs with suspicious repetitive patterns.
RATE LIMITING

Apply per-user/session rate limits on agent invocations to reduce DoS throughput.
DETECTION

Alert on sustained high CPU usage in agent worker processes; log parsing duration and alert on outliers >500 ms.
INVENTORY

Audit all internal and customer-facing apps that import langchain.agents.mrkl.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable Yes

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

DoS Prompt Injection Framework Agent AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0034 - Cost Harvesting AML.T0043 - Craft Adversarial Data AML.T0049 - Exploit Public-Facing Application AML.T0051 - LLM Prompt Injection AML.T0051.000 - Direct AML.T0051.001 - Indirect

Compliance Impact

This CVE is relevant to:

EU AI Act

Art. 15 - Accuracy, robustness and cybersecurity Article 15 - Accuracy, Robustness, and Cybersecurity

ISO 42001

A.6.1.4 - AI Risk Assessment A.6.2.6 - AI system availability and resilience A.9.2 - AI Incident Handling A.9.3 - AI system risk treatment

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain the deployed AI system MAP-5.1 - Likelihood and Impact of AI Risks

OWASP LLM Top 10

LLM01:2025 - Prompt Injection LLM07:2025 - System Prompt Leakage / Insecure Output Handling LLM10:2025 - Unbounded Consumption

Frequently Asked Questions

What is CVE-2024-58340?

Any LangChain-based application running MRKL agents on version 0.3.1 or earlier is vulnerable to a DoS attack delivered via prompt injection — no authentication required. An attacker who can influence LLM output (e.g., through user-supplied prompts in a downstream app) can stall your agent service with a single crafted string. Patch to LangChain >0.3.1 immediately; if you cannot patch today, wrap MRKLOutputParser calls with a timeout and sanitize LLM output before parsing.

Is CVE-2024-58340 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-58340, increasing the risk of exploitation.

How to fix CVE-2024-58340?

1. PATCH: Upgrade langchain to the first version past 0.3.1 that includes the fixed regex; verify with `pip show langchain`. 2. WORKAROUND (if patch is not immediate): Wrap MRKLOutputParser.parse() calls with a signal-based or thread-based timeout (e.g., 2–5 seconds); raise a parsing error and abort on timeout. 3. INPUT HYGIENE: Truncate LLM output to a reasonable maximum length (e.g., 4 KB) before passing to the parser; reject outputs with suspicious repetitive patterns. 4. RATE LIMITING: Apply per-user/session rate limits on agent invocations to reduce DoS throughput. 5. DETECTION: Alert on sustained high CPU usage in agent worker processes; log parsing duration and alert on outliers >500 ms. 6. INVENTORY: Audit all internal and customer-facing apps that import langchain.agents.mrkl.

What systems are affected by CVE-2024-58340?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, LLM agentic pipelines, ReAct/MRKL agent workflows, multi-tenant LLM SaaS.

What is the CVSS score for CVE-2024-58340?

CVE-2024-58340 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.08%.

Technical Details

NVD Description

LangChain versions up to and including 0.3.1 contain a regular expression denial-of-service (ReDoS) vulnerability in the MRKLOutputParser.parse() method (libs/langchain/langchain/agents/mrkl/output_parser.py). The parser applies a backtracking-prone regular expression when extracting tool actions from model output. An attacker who can supply or influence the parsed text (for example via prompt injection in downstream applications that pass LLM output directly into MRKLOutputParser.parse()) can trigger excessive CPU consumption by providing a crafted payload, causing significant parsing delays and a denial-of-service condition.

Exploitation Scenario

An attacker targets a public-facing AI assistant built on LangChain MRKL agents. They craft a user prompt designed to cause the underlying LLM to produce output containing a pathological string — for example, a long sequence of spaces or repeated characters that exploits the backtracking in the MRKL action-extraction regex (e.g., `Action: ` followed by thousands of repeated ambiguous characters). The application passes the LLM's raw output directly to MRKLOutputParser.parse() without sanitization. The regex engine enters catastrophic backtracking, pegging one CPU core at 100% for tens of seconds per request. An attacker automating dozens of such requests can exhaust worker threads and render the service unavailable for all users within minutes.