Open-Weight LLM Vulnerability Analysis

• Open-weight LLM vulnerability analysis is a comprehensive review of cyber threat vectors, evaluation methodologies, and risk quantification in accessible models.
• Empirical studies reveal multi-turn prompt injection success rates up to 92.78%, highlighting critical gaps in existing safety mechanisms.
• The research proposes technical, operational, and regulatory safeguards to mitigate systemic vulnerabilities throughout the LLM lifecycle.

Open-weight LLMs are neural models released with fully accessible weights, enabling unrestricted local deployment, inspection, modification, and fine-tuning. While their open nature accelerates innovation, customization, and research, it simultaneously exposes significant cyber risk and systemic security challenges. Vulnerability analysis of open-weight LLMs encompasses adversarial content elicitation (e.g. prompt injection, jailbreaks), explicit unsafe code generation, and classical software supply-chain flaws across the entire model lifecycle. This article presents a comprehensive survey of cyber threat vectors, evaluation methodologies, operational risks, technical mitigations, and principal research findings on open-weight LLM vulnerabilities, as established in recent literature.

1. Cyber-Risk Taxonomy: Attack Vectors and Threat Surfaces

Open-weight LLMs introduce unique digital offense and defense asymmetries. The following taxonomy, adapted from (Gregorio, 21 May 2025), delineates the principal cyber-risk vectors:

1. Automated and Personalized Social Engineering
   • Phishing at scale (e-mail, SMS, chatbots)
   • Deepfake voice and synthetic media lures
   • Micro-targeting exploiting public/breached data
2. Accelerated Malware and Tool Development
   • Autonomous generation of malware (ransomware, trojans, keyloggers)
   • Code translation/adaptation for evasion from signature-based detection
   • Rapid prototyping, debugging, and exploit mutation cycles
3. Automated Vulnerability Discovery and Exploitation
   • AI-assisted fuzzing and symbolic execution to discover zero-days
   • Large-scale codebase pattern recognition (root cause mining)
   • Automated exploit-chain synthesis (e.g. LLMs chained with CVE scanners)
4. Evasion and Obfuscation
   • Generation of polymorphic/metamorphic malware
   • Adversarial perturbations crafted to bypass AI security detectors
   • Automated binary packing/unpacking routines
5. Disinformation and Misinformation
   • Mass generation of authentic-seeming but false narratives
   • Auto-translation and localization to expand attack reach
   • Large-scale “firehose” amplification on social platforms

These risk vectors are magnified by open-weight release: offline fine-tuning or prompt engineering enables adversaries to bypass alignment constraints present in closed, API-gated models.

2. Empirical Evaluation and Vulnerability Metrics

Recent empirical studies deploy systematic adversarial testing and risk quantification frameworks:

Adversarial Prompting and Jailbreak Analysis

• (Chang et al., 5 Nov 2025) introduces a black-box threat model evaluating eight open-weight LLMs with single-turn and multi-turn prompt injections. Models such as Meta Llama 3.3-70B Instruct and Mistral Large-2 Instruct exhibit multi-turn prompt injection success rates up to 92.78%; multi-turn attacks are 2× to 10× more effective than single-turn baselines.
• Techniques include Crescendo Escalation, Information Decomposition & Reassembly, Role-Play, Contextual Ambiguity, and Refusal Reframe, with multi-turn attacks routinely bypassing safety guardrails.

Risk Scoring and Policy-Oriented Metrics

• (Gregorio, 21 May 2025) adapts the MITRE OCCULT evaluation targeting the offensive capabilities of open-weight LLMs:

$\text{risk}_v = C_v \times E_v \times P_v$

where $C_v$ = capability score, $E_v$ = exploitability, $P_v$ = prevalence for attack vector $v$. Aggregate risk is policy-weighted:

$$R_{\text{total}} = \sum_{v \in V} w_v \cdot \text{risk}_v$$

DeepSeek-R1 achieves >90% accuracy on the TACTL-183 cyber-operations benchmark.

Severity-Weighted Generation Exposure

• (Vallez et al., 6 Nov 2025) defines Prompt Exposure (PE) and Model Exposure (ME) scores for LLM-generated vulnerabilities:

$\mathrm{PE}_x = \max\!\Bigl(0,\;\log_b\!\Bigl(\frac{1}{N+1}\sum_{y\in\Phi_x} b^{\widehat{\mathrm{CVSS}_y\;\cdot\;P_y\;\cdot\;R_y}\Bigr)\Bigr)$

$$\mathrm{ME} = \log_b\!\Bigl(\frac{1}{|\Theta|}\sum_{x\in\Theta} b^{\,\mathrm{PE}_x}\Bigr)$$

ME encapsulates both prevalence and severity (CVSS-mapped) for a practical, scenario-driven risk assessment.

Code Vulnerability Assessment

• (Guo et al., 29 Aug 2024, Yin et al., 2 Apr 2024) evaluate open-weight LLMs on binary and multiclass software vulnerability detection, localization, and severity assessment benchmarks such as Devign, LineVul, Big-Vul, and custom CWE/CVE datasets. Fine-tuned models demonstrate high accuracy on in-distribution data, but generalization is limited. Pre-trained LLMs underperform domain-specific transformers and graph neural nets in recall and precision under cross-dataset evaluation.

Supply Chain Security

• (Wang et al., 18 Feb 2025) provides a taxonomy and large-scale analysis of 529 CVEs across the LLM lifecycle (data, model, application layers). Application and model layers account for >93% of supply chain vulnerabilities, with improper resource control (45.7%) and improper neutralization/injection (25.1%) as dominant root causes.

3. Case Studies and Empirical Findings

Studies document concrete vulnerabilities and systemic gaps:

Threat Vector	Open-Weight Example	Closed-Source Contrast
Social Engineering	DeepSeek-R1 generates personalized phishing campaigns (Gregorio, 21 May 2025)	APIs sanitize/refuse or limit high-risk prompts
Malware Generation	Xanthorox AI auto-generates C2 beacons with obfuscation; infinite, unsupervised use (Gregorio, 21 May 2025)	API-gated models employ payload refusal and watermark
Vulnerability Discovery	DeepSeek-R1 fine-tuned discovers novel zero-day buffer overflows in <30 min (Gregorio, 21 May 2025)	Traditional tools require days, expert oversight
Guardrail Evasion	“BadLLama3” fork removes RLHF guardrails in <10 minutes, generates shell-evasion code (Gregorio, 21 May 2025)	Closed systems: no local control, strong monitoring
Prompt Injection	Llama 3.3 and Qwen 3 models reach ~90% multi-turn injection success (Chang et al., 5 Nov 2025)	Lower multi-turn success in safety-oriented models
Explicit Vulnerability	Qwen2, Gemma, Mistral output requested C vulnerabilities at >70% accuracy via specific prompt templates (Bosnak et al., 14 Jul 2025)	No real-time safety enforcement at code generation

Empirical evidence demonstrates that open-weight models frequently generate exploitable code patterns, including buffer overflows, improper validation, and hard-coded secrets. Typical alignment and refusal filters are easily circumvented in local or fine-tuned deployments (Chang et al., 5 Nov 2025, Bosnak et al., 14 Jul 2025).

4. Scanner Technologies and Evaluation Ecosystem

Multiple open-source scanners, red-teaming pipelines, and security benchmarks have emerged to audit LLM vulnerabilities (Brokman et al., 21 Oct 2024):

• Garak: Static attacker prompts with rule-based evaluation; low customizability.
• Giskard: Hybrid static/LLM attack-eval workflows; high customizability and multi-language support.
• PyRIT: Fully LLM-driven attacker/evaluator; supports multi-turn, goal-oriented tests.
• CyberSecEval: Focused on code-integrity; uses static scanners (Semgrep, regex) for CWE vulnerability matching.

All scanners quantify attack success rate (ASR):

$\text{ASR} = \frac{S}{N}$

where $S$ is the number of successful adversarial prompts (scanner-evaluated), $N$ the total tried. Evaluator error rates (MOE) may reach 26%, with LLM-based evaluators more reliable than static matchers but susceptible to instruction drift.

A ground-truth, 1,000-example, manually labeled dataset serves as a reference to calibrate evaluator reliability and future scanner benchmarks.

5. Regulatory and Supply Chain Perspectives

Conventional regulatory frameworks (EU AI Act, GPAI Code of Practice) inadequately address open-weight LLM risks due to fundamental loss of distribution control (Gregorio, 21 May 2025):

• The EU AI Act’s open-source exemption (Art. 2(10e)) is ambiguously defined; narrow interpretation restricts innovation, while a loose view exempts even high-risk weights from scrutiny.
• Post-market monitoring and model provenance become infeasible post-release; adversaries can trivially remove/alter alignment constraints, watermarks, and refuse attribution.
• Security is further imperiled by classical software supply chain vulnerabilities: 50.3% of vulnerabilities emerge in the application layer, 42.7% in the model layer, highlighting front-end, orchestration, and model management attack surfaces (Wang et al., 18 Feb 2025).

Patches are often ineffective; 8% of vulnerabilities reoccur due to incomplete mitigation (notably with path traversal and code injection flaws).

6. Defense, Assessment Pipelines, and Recommendations

Robust open-weight LLM security requires multi-layered interventions:

• Technical Controls:
  • Capability-level evaluation and gating prior to weight release: only core, non-sensitive weights are open; exploit-specific modules are access-gated (Gregorio, 21 May 2025).
  • Tamper-evident watermark embedding for forensic provenance; model beacon clients for distributed usage monitoring.
  • Defensive AI: network/host anomaly detection using LLM-powered inference, automated LLM-driven incident response, and AI-enhanced vulnerability scanning.
• Operational Practice:
  • Security-first model and deployment selection; preference for safety-oriented design (persistent guardrails, defense-in-depth).
  • Layered defenses: strict meta-prompts, runtime context filters, logging, and anomaly detection.
  • Continuous adversarial assessment—systematic multi-turn, multi-technique red-teaming (Chang et al., 5 Nov 2025).
• Assessment Pipeline (per (Bosnak et al., 14 Jul 2025)):
  1. Controlled dynamic and reverse prompt injection (multiple vulnerability types, personas, directness).
  2. Automated static (e.g. ESBMC) or symbolic vulnerability analysis of outputs.
  3. Statistical tracking of vulnerability rate and accuracy; audit for systematic filter bypass patterns.
  4. Threshold-based remediation triggers (e.g., incidence of vulnerable completions, misalignment rate).
  5. Periodic retraining and classifier updating.

• Policy and Ecosystem:

  • International CTI (cyber threat intelligence) sharing (ENISA, CISA, NATO CCDCOE) for AI-driven threat response (Gregorio, 21 May 2025).
  • Standardized benchmarks and evaluator accuracy requirements for LLM vulnerability scanners (Brokman et al., 21 Oct 2024).
  • Explicit reporting and exposure metrics (PE, ME) for models to supplement code functionality metrics (Vallez et al., 6 Nov 2025).
  • Downstream liability frameworks for sector-specific risks (finance, healthcare).

7. Open Challenges and Future Directions

Despite empirical and methodological advances, the following challenges persist:

• Alignment retention is shallow—persistent prompt injection/jailbreak vulnerabilities are endemic, and multi-turn attacks overwhelm current defenses (Chang et al., 5 Nov 2025).
• Real-world code scenarios reveal that fine-tuning and advanced prompt engineering can induce explicit reproduction of severe, well-known vulnerabilities, even with “benign” educational personas (Bosnak et al., 14 Jul 2025).
• Cross-benchmark generalization is weak: LLMs excel “in distribution” but collapse outside training data (macro-F1 drops from >60% to 20–50% on unseen splits) (Guo et al., 29 Aug 2024).
• Existing detection infrastructure suffers from evaluator unreliability, high MOE, and lack of explainability. Manual review remains critical for ambiguous or low-signal outputs (Brokman et al., 21 Oct 2024).
• Dataset mislabeling, incomplete context, and class imbalance impede both model training and reliable risk evaluation (Guo et al., 29 Aug 2024).
• Patching and CVE remediation processes lag behind exploit adaptation, with significant recurrences for resource-control and injection vulnerabilities (Wang et al., 18 Feb 2025).
• No consensus exists on model exposure “budgets,” or trade-off calibration between functional correctness and risk of unsafe generation (Vallez et al., 6 Nov 2025).

Key future directions include the integration of dynamic and symbolic runtime analysis into vulnerability pipelines, hybrid human-in-the-loop auditing for near-threshold samples, supply chain taint analysis, and adoption of security–functionality negotiating metrics (PE, ME) in both research and operational model releases.

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Open-weight LLM vulnerability analysis reveals fundamental trade-offs between openness, innovation, and systemic cyber risk. Only through technical, operational, and regulatory co-evolution—anchored in standardized metrics, rigorous assessment, and proactive cross-sector cooperation—can the community realize the potential of open-weight LLMs while mitigating the risks inherent in their unfettered use.