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CVSS estimates technical severity, but it does not automatically equal business risk. Prioritize using context like internet exposure, asset criticality, known exploitation, and whether compensating controls exist. A Medium CVSS on an exposed production system can be more urgent than a Critical on an isolated non-production host.

Vulnerability Database

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A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.

Q: What's the difference between a vulnerability, an exploit, and a zero-day?

A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. Risk increases sharply when exploitation becomes reliable or widespread.

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Recurring findings usually come from incomplete asset discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host.

Q: How do we prioritize remediation without burning out the team?

Use a repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress so remediation is steady, not reactive.

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352,262

Total vulnerabilities in the database

CVE-2026-52879 — github.com/klever-io/klever-go

Uncontrolled Resource Consumption

Summary

networkMessenger.directMessageHandler in network/p2p/libp2p/netMessenger.go spawns a fresh goroutine for every incoming direct message before the antiflood layer makes an admission decision. There is no semaphore, throttler, or bound on concurrent in-flight spawns.

A single connected libp2p peer can open a DirectSendID stream and send well-formed TopicMessage envelopes with varying sequence numbers. Each accepted direct message reaches directMessageHandler and triggers a fresh goroutine before processor.ProcessReceivedMessage runs. This allows unbounded goroutine growth and node availability degradation from one peer.

This remains present in the latest release v1.7.17: network/p2p/libp2p/netMessenger.go:1060 still spawns go func(msg p2p.MessageP2P) before processor.ProcessReceivedMessage. I also verified current develop commit 10bcfd50, where the same spawn remains at network/p2p/libp2p/netMessenger.go:1115.

This is distinct from GHSA-74m6-4hjp-7226 and GHSA-87m7-qffr-542v. Those advisories concern MultiDataInterceptor decompression/throttler behavior. This report concerns the libp2p direct-message ingress wrapper spawning an unbounded goroutine before processor-level antiflood/admission logic runs. A patch to Batch.Decompress or MultiDataInterceptor does not bound this direct-message goroutine spawn.

Details

The affected path is network/p2p/libp2p/netMessenger.go in directMessageHandler.

The direct-message path transforms and validates the message, looks up the topic processor, then immediately spawns a goroutine:

func (netMes *networkMessenger) directMessageHandler(message *pubsub.Message, fromConnectedPeer core.PeerID) error {
    var processor p2p.MessageProcessor

    topic := *message.Topic
    msg, err := netMes.transformAndCheckMessage(message, fromConnectedPeer, topic)
    if err != nil {
        return err
    }

    netMes.mutTopics.RLock()
    processor = netMes.processors[topic]
    netMes.mutTopics.RUnlock()

    if processor == nil {
        return fmt.Errorf(&quot;%w on directMessageHandler for topic %s&quot;, p2p.ErrNilValidator, topic)
    }

    go func(msg p2p.MessageP2P) {
        if check.IfNil(msg) {
            return
        }

        errProcess := processor.ProcessReceivedMessage(msg, fromConnectedPeer)
        // ...
    }(msg)

    return nil
}

The processor-level antiflood decision happens inside ProcessReceivedMessage, after the goroutine, its stack, and the cloned message reference already exist. That means antiflood can bound processing rate, but not goroutine creation rate.

The existing goRoutinesThrottler with capacity broadcastGoRoutines = 1000 is wired into outgoing broadcast paths such as BroadcastOnChannelBlocking, not this incoming direct-message path.

The parallel pubsub ingress path in the same file handles a comparable inbound message surface synchronously:

err = handler.ProcessReceivedMessage(msg, fromConnectedPeer)

So the direct-message path is asymmetric: same transform/check function, same ProcessReceivedMessage callee, but direct-message ingress adds an unbounded goroutine spawn.

Reachability:

directSender.go registers DirectSendID as a libp2p stream protocol.
directStreamHandler reads framed pubsub.Message envelopes from the stream.
directStreamHandler forwards each message to networkMessenger.directMessageHandler.
Any connected peer can send well-formed envelopes to registered topics.
The seenMessages cache keys on From + Seqno; Seqno is attacker-controlled in the envelope, so incrementing it bypasses dedupe.

PoC

GitHub Private Vulnerability Reporting does not appear to allow file attachments in this form, so I am including the reproduction command and captured output inline. I can provide the full Go test file immediately if useful.

The PoC is a Go test file intended to be placed under network/p2p/libp2p/ in a klever-go checkout. It exercises the real network/p2p/libp2p package with NewMockMessenger.

Reproduction:

git clone https://github.com/klever-io/klever-go
cd klever-go
git checkout v1.7.16

# Place dos_directmsg_test.go into:
# network/p2p/libp2p/

go test ./network/p2p/libp2p/ -run TestPoC_ -count=1 -v -timeout 60s

Captured output:

=== RUN   TestPoC_DirectMessageHandler_SpawnsGoroutinePerMessage
    baseline goroutines: 43
    peak goroutines after 500 direct messages: 543 (delta = 500)
    final goroutines after drain + GC: 43
POC_RESULT direct=spawn N=500 baseline=43 peak=543 delta=500 threshold=400 final=43
--- PASS

=== RUN   TestPoC_SynchronousHandler_NoGoroutineGrowth
    baseline goroutines: 47
    peak goroutines after 500 synchronous calls: 47 (delta = 0)
POC_RESULT sync=block N=500 baseline=47 peak=47 delta=0
--- PASS

=== RUN   TestPoC_DirectMessageHandler_NoThrottlerInPath
    all 2000 SendToConnectedPeer calls returned in 2.490708ms -- no throttler blocking
POC_RESULT throttler=absent N=2000 elapsed=2.490708ms
--- PASS

Reading:

500 direct messages with slow processors produced exactly 500 new goroutines.
The synchronous control path produced zero goroutine growth.
2000 messages, twice the outgoing broadcastGoRoutines = 1000 capacity, returned immediately, showing no ingress throttler blocks this path.

Impact

A single connected peer can sustain unbounded goroutine spawn growth on a klever-go node. Each spawned goroutine allocates its own stack, holds message references until the processor returns, and adds scheduler and GC pressure before antiflood admission can reject the message.

Under realistic attacker line rate and non-trivial processor latency, goroutine count can grow faster than the runtime drains it, degrading the node's ability to process legitimate traffic. This maps to the SECURITY.md High category: "Denial of Service affecting network availability."

All testing was local only. I did not contact Klever mainnet, public testnet, hosted RPCs, explorers, or third-party production infrastructure.

Suggested fixes:

Wire goRoutinesThrottler.CanProcess() or a dedicated ingress throttler before the go func() spawn in directMessageHandler.
Or remove the goroutine and call ProcessReceivedMessage synchronously, matching the existing pubsubCallback path.

Disclosure note: I originally sent this report to [email protected] on 2026-05-13. Since SECURITY.md lists GitHub Private Vulnerability Reporting as the recommended channel, I am resubmitting it here.

Published: Jun 5, 2026
Updated: Jun 10, 2026
CVE: CVE-2026-52879
GHSA: GHSA-hf2g-6j7h-98wg
Severity: High
Exploit:
CISA KEV:

CVSS v3:

Severity: High
Score: 7.5
AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CWEs:

Affected Software
References

Software	From	Fixed in
github.com/klever-io/klever-go	1.7.14	1.7.18

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Frequently Asked Questions

What is a Vulnerability (CVE) and why does it matter?

A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.

CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.

A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. In practice, risk increases sharply when exploitation becomes reliable or widespread.

Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.

Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.

SynScan combines attack surface monitoring and continuous security auditing to keep your inventory current, flag high-impact vulnerabilities early, and help you turn raw findings into a practical remediation plan.